United States WJt-600 /2-'87$02
Environmental Protection -'->
*Be"cv . November 198T%
&EPA Research and
Development
EVALUATION OF
THE EFFECTIVENESS OF
CHEMICAL DUST SUPPRESSANTS
ON UNPAVED ROADS
Prepared for
EPA Region 5
Prepared by
Air and Energy Engineering Research
Laboratory
Research Triangle Park NC 27711
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EPA-600/2-87-102
November 1987
EVALUATION OF THE EFFECTIVENESS OF CHEMICAL DUST
SUPPRESSANTS ON UNPAVED ROADS
By
G. E. Muleski and C. Cowherd, Jr.
Midwest Research Institute
425 Volker Boulevard
Kansas City, Missouri 64110
Funded By:
LTV Steel Company, Inc.
Penalty Credit Project 5200-868236
EPA Project Officer;
Robert C. McCrillis
Air and Energy Engineering Research Laboratory
Research Triangle Park, North Carolina 27711
Prepared For;
U, S. Environmental Protection Agency
Office of Research and Development
Washington, DC 20460
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PREFACE
This report was prepared by Midwest Research Institute (MRI) under LTV
Steel Company, Inc. (formerly, Jones and Laugh!in Steel Corporation) Pur-
chase Order No. 5200-868236. Tht penalty credit project described herein
was directed by the U.S. Environmental Protection Agency, Air and Energy
Engineering Research Laboratory (Robert C. McCrillis, Project Officer).
All work was performed in MRI's Air Quality Assessment Section (John
Kinsey, Head).
The authors of this report are Gregory E. Muleski, Project Leader,
and Chatten Cowherd, Jr. Field sampling was conducted under the direc-
tion of Frank Pendleton and Muleski with assistance from David
Griffin, Julia Hoffmeister, Phillip Englehart, and Scott Smith.
Additional laboratory analysis was performed by Stephen Cummins.
ii
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CONTENTS
Page
Preface. 11
Figures -• • iv
Tables v
Summary and Conclusions. . ..... vi
1.0 Introduction 1
1.1 Program objectives 2
1.2 Report structure .... 2
2.0 Selection of Control Measures, Test Sites, Study
Design, and Description of test Methodology 3
2.1 Characterization of unpaved road traffic .... 3
2.2 Control measure selection 7
2.3 Test site selection 7
2.4 Selection of study design 11
2.5 Quality assurance 14
2.6 Air sampling equipment and technique 14
2.7 Emission testing procedure 18
2.8 Aggregate material sampling and analysis .... 29
2.9 Auxiliary equipment and samples 29
3.0 Chronology of the Field Testing Program and Test
Results 33
3.1 Modifications to test plan 33
3.2 Source description , 34
3.3 Control applications ...... 37
3.4 Results of the exposure profiling tests 39
4.0 Control Efficiencies and Cost Effectiveness Values .... 58
4,1 Comparisons involving only one chemical 59
4.2 Average control efficiency . 60
4.3 Interchemical comparisons 60
4.4 Relative cost-effectiveness of the
suppressants evaluated 63
4.5 Examination of alternative indicators of
control performance 66
5.0 Control Performance Model Development 69
5.1 Objectives of the average control performance
model. ........ 69
5.2 Review of previous studies 70
5.3 Average control model for petroleum resins ... 73
6.0 References 76
7.0 Glossary 78
8.0 English to Metric Unit Conversion Table 81
m
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FIGURES
Number Page
SC-1 Average control efficiency as a function of time over
30 days viii
2-1 Copy of IN-TECH summary data of vehicle miles traveled
in iron and steel industry 4
2-2 Copy of IN-TECH summary data of iron and steel unpaved
road traffic by vehicle type 5
2-3 Copy of IN-TECH summary data of average weights for
vehicle types on iron and steel unpaved roads 6
2-4 MRI exposure profiler 16
2-5 Cyclone preseparator/cascade intpactor combination. .... 17
3-1 Test site at plant AP 35
3-2 Test site at plant AQ 36
3-3 Variation of surface material properties (before control)
over test sections (see text for mean values) 40
3-4 Cumulative rainfall and chronology of events at
plant AQ 45
4-1 Average control efficiency as a function of time over
30 days 61
4-2 Relationship between controlled PM10 emission factors
and silt loading. Arrows indicate chronology of
testing 68
5-1 Average control performance model for petroleum resins . . 74
IV
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TABLES
Number Page
2-1 Unpaved Road Dust Control Survey Results 8
2-2 Dust Suppressants Recently Used or Considered for
Evaluation 10
2-3 Air Sampling Equipment 15
2-4 Quality Assurance Procedures for Sampling Media 19
2*5 Quality Assurance Procedures for Sampling Flow Rates ... 20
2-6 Quality Assurance Procedures for Sampling Equipment. ... 21
2-7 Criteria for Suspending or Terminating an Exposure
Profiling Test 22
2-8 Moisture Analysis Procedures 30
2-9 Silt Analysis Procedures 31
3-1 Application Parameters - Plant AP 38
3-2 Application Parameters - Plant AQ 38
3-3 Test Site Parameters - Plant AP 41
3-4 Test Site Parameters - Plant AQ 42
3-5 Representative Concentrations (ug/m3) - Plant AP 46
3-6 Representative TP Concentrations (ug/m3) - Plant AQ. . . . 47
3-7 Isokinetic Correction Parameters - Plant AP 48
3-8 Isokinetic Correction Parameters - Plant AQ 49
3-9 Aerodynamic Particle Size Data - Plant AP 50
3-10 Aerodynamic Particle Size Data - Plant AQ 51
3-11 Plume Sampling Data - Plant AP 53
3-12 Plume Sampling Data - Plant AQ 54
3-13 Surface Properties and Emission Factors - Plant AP . . . . 55
3-14 Surface Properties and Emission Factors - Plant AQ . . . . 56
5-1 Summary of Major Unpaved Road Control Efficiency Tests
Performed at Iron and Steel Plants 71
5-2 Average Control Efficiency from Tests of Petroleum
Resins in the Iron and Steel Industry 72
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SUMMARY AND CONCLUSIONS
The purpose of this study was to obtain data characterizing the average
control performance of dust suppressants commonly used by the iron and steel
industry to mitigate particulate emissions from unpaved roads. Vehicular
traffic on unpaved roads has been estimated to contribute more than half of
the suspended particulate emissions from open sources in the industry.
Control efficiency values were determined not only for total particu-
late (TP), but also for particles less than 15 urn in aerodynamic diameter
(inhalable particulate, IP), less than 10 urn in aerodynamic diameter (PM10),
and less than 2,5 urn in aerodynamic diameter (fine particulate, FP), The
study focused on PMio control performance of dust suppressants in particu-
lar, because this size fraction is anticipated to form the basis of any re-
vised National Ambient Air Quality Standard for particulate matter.
In order to make the control performance test results as useful as pos-
sible to the industry, unpaved road vehicular traffic characteristics and
dust control techniques used in the industry were surveyed early in the
study. Subsequently these results formed the basis for the design of the
field testing program so that commonly used suppressants could be evaluated
under service conditions representative of typical iron and steel industry
unpaved roads.
The exposure profiling method developed by MRI was the technique uti-
lized to measure uncontrolled and controlled emission factors for vehicular
traffic on unpaved roads. Exposure profiling of roadway emissions involves
direct isokinetic measurement of the total passage of open dust emissions
approximately 5 m downwind of the edge of the road by means of simultaneous
sampling at four points distributed vertically over the effective height of
the dust plume. Downwind particle size distributions were measured using
cyclone precollectors followed by parallel slot cascade impactors. Upwind
particle size distributions were also determined using impaction. A total
of 64 tests of controlled and uncontrolled particulate emissions from vehic-
ular traffic on unpaved roads were conducted at two iron and steel plants.
Five chemical dust suppressants were evaluated during the study:
Petro Tac, an emulsified asphalt
Coherexf, a petroleum resin
Soil-Sement, an acrylic cement
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Generic 2 (QS), a generic petroleum resin product developed at the
Mellon Institute
liquidow , a salt (calcium chloride)
All products, with the exception of Generic, have been used in iron and
steel plants. In addition, industry personnel have expressed considerable
interest in the use of Generic.
These suppressants were applied under the direction of MRI personnel in
quantities that generally spin the range of common practice in the industry,
manufacturers1 recommendations, and previous field evaluations. Control ef-
ficiency measurements were made over periods up to 70 days after applica-
tion, although the main averaging period of interest was approximately
1 month. The latter is representative of time periods between control ap-
plications in the industry.
Average control efficiencies over the first 30 days for specific parti-
cle size ranges are presented in Figure SC-1. Note that code letters (ex-
plained in the text) have been assigned to the various dust suppressants in
order to discourage selective citation of test results. It is recommended
that the report taken as a whole be used as a basis for decisions regarding
dust control programs rather than any one data set taken independently,
All chemicals tested exhibited average control efficiencies of approxi-
mately 50% or more over the first 30 days after application. These tests
were conducted using application and traffic parameters that may be consid-
ered typical in the iron and steel industry. Note that while the control
provided by some suppressants showed significant temporal decay, others ex-
hibited a relatively constant level of control over the time period.
Statistical analyses of the data indicate that reapplication results
in a significantly higher level of control and that only one suppressant
exhibited significant differences in control between the various particle
size fractions. Comparisons between the control efficiencies for different
chemicals indicate that there were relatively few suppressant/size fraction
combinations which could be considered significant at the 5% level.
Comparison of the relative cost-effectiveness reveals only a slight
variation between the suppressants other than calcium chloride. In terms of
cost-effectiveness, the salt did not compare favorably with the other prod-
ucts; however, this is at least a partial result of the abnormally high pre-
cipitation during the field exercise.
Several road surface material properties were discussed as possible
indicators of control performance. While reasonably strong relationships
between silt loading and control were found for some of the suppressants,
the clustered nature of the entire data set precluded development of a re-
liable performance indicator. However, the data suggest that the indus-
trial paved road emission factor equation may be used to conservatively
overestimate emissions from controlled unpaved roads.
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AQ-/ +o -&
10
IO
AFTER APPI./CAT/CM
Figure SC-1. Average control efficiency as a function of time over 30 days.
vm
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Finally, results of previous tests were combined with data from the
present study to develop an average control performance model for petroleum
resins. The model was designed to meet typical needs in the iron and steel
industry in terms of averaging periods and service environments.
IX
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SECTION 1.0
INTRODUCTION
Numerous prior studies of the iron and steel industry1 4 have shown
that open dust sources (such as vehicular traffic on paved and unpaved
roads, material handling and wind erosion) merit prime consideration in the
development of particulate emission control strategies. This conclusion has
been based on (a) industry-wide comparisons between uncontrolled emissions
from open dust sources, and (b) typically controlled fugitive emissions from
major process sources such as steel-making furnaces, blast furnaces, coke
ovens, and sinter machines. In addition, preliminary cost-effectiveness
(dollars expended per unit mass of reduced particulate emissions) analysis
of promising control options for open dust sources has indicated that con-
trol of these sources might result in significantly improved air quality at
a lower cost compared to the control of process sources.
Of open dust sources, vehicular traffic on paved and unpaved roads gen-
erally account for the vast majority of particulate emissions in the iron
and steel industry. For the 1970's, unpaved surfaces were estimated to ac-
count for roughly 70% of open source particulate emissions in the industry.2
By the early 1980's the contribution was considerably smaller. This reduc-
tion was due to implementation of dust control programs which, in addition
to chemical treatment of unpaved roads, included paving numerous roads and
using shuttle buses to reduce emissions from employees commuting to their
work stations.
Some unpaved roads in the iron and steel industry are, by their nature,
not suitable for paving. These roads are normally used by very heavy vehi-
cles or may be subjected to considerable spillage. Because of the additional
maintenance costs associated with a paved road under this type of service
environment, emissions from these roads generally are controlled with regu-
lar reapplications of chemical treatments.
Besides water, petroleum resins (such as Coherex®) have historically
been the products most widely used in the industry; however, considerable
interest has been shown at both the plant and corporate level in alternative
chemical dust suppressants. As a result of this continued interest, several
new dust suppressants have been introduced recently. These have included
asphalt emulsions, acrylics, salts, and adhesives. In addition, the generic
petroleum resin formulations developed at the Mellon Institute with funding
from the American Iron and Steel Institute (AISI) have gained considerable
attention. These generic suppressants were designed to be produced on-site
at iron and steel plants.5
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1.1 PROGRAM OBJECTIVES
The overall objective of this study was to provide data that document
the reduction of participate emissions (in several particle size ranges)
generated by vehicular traffic on representative unpaved roads in the iron
and steel industry following control application. The data were used to
provide average control efficiencies for common road dust suppressants^ over
ranges of averaging periods and application parameters that span typical
values used in the iron and steel industry. Information on this type is
valuable to both industry and regulatory personnel in developing and moni-
toring dust control programs.
In addition, there were several secondary objectives which largely sup-
ported the primary objective stated above. These included: (a) a survey
of current and projected industry practices in unpaved road dust control;
(b) characterization of traffic on unpaved roads in the industry; (c) col-
lection of cost data to develop relative cost-effectiveness values for the
suppressants evaluated; (d) examination of less expensive measures to moni-
tor control performance; and (e) analysis of the current previous studies
in order to develop a model to estimate control performance.
1.2 REPORT STRUCTURE
The report is structured as follows; (a) Section 2.0 focuses on the
methodology used to quantify road dust controls used in the iron and steel
industry; (b) Section 3.0 presents and discusses the results of source test-
ing by exposure profiling; (c) Section 4.0 discusses control efficiency and
cost-effectiveness values for the dust suppressants evaluated; and (d) Sec-
tion 5.0 discusses a model developed to estimate average control performance
as a function of application parameters. Sections 6.0 and 7.0 presents the
references and a glossary, respectively. i
This report contains both metric and English units. In the text, most
numbers are generally reported in metric units with English units in paren-
theses. For numbers commonly expressed in metric units in the air pollution
field (e.g., particle size in urn, density in g/cm3, and concentration in
Mg/m3), no English equivalent is given. A conversion table is given as
Section 8.0.
Finally, the particle size ranges used in this report are:
TP Total airborne particulate matter.
IP Inhalable particulate matter consisting of particles smaller
than IS pm in aerodynamic diameter. :
PM10 Particulate matter consisting of particles smaller than
10 urn in aerodynamic diameter. !
FP Fine particulate matter consisting of particles smaller than
2.5 (jm in aerodynamic diameter.
Particular attention is paid to the PM10 size fraction, in anticipation of
possible revision of National Ambient Air Quality Standards for particulate
matter.
2
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SECTION 2.0
SELECTION OF CONTROL MEASURES, TEST SITES, STUDY DESIGN,
AND DESCRIPTION OF TEST METHODOLOGY
This section describes how specific unpaved road dust suppressants
were selected for testing and how the test conditions were determined.
Also, the selection criteria for test sites and the study design are re-
viewed. Finally the detailed test methodology, including air and surface
material sampling and analysis, is described.
2.1 CHARACTERIZATION OF UNPAVED ROAD TRAFFIC
During recent years, the iron and steel industry has paved many pre-
viously unpaved roads used primarily by light-duty vehicles (e.g., automo-
biles, pickup trucks), while roads used by heavy-duty trucks have largely
remained unpaved. In addition, a good deal of light duty traffic has been
eliminated by employee bussing programs. In order to design the testing
program so that the test results would be as useful as possible, a study
was conducted to determine the relative importance of light-duty vehicles
on unpaved roads in the industry.
IN-TECH of Pittsburgh, Pennsylvania was retained to provide summary
data collected during traffic studies of paved and unpaved roads at nine
different steel plants east of the Mississippi River. These data are pre-
sented in Figures 2-1, 2-2, and 2-3.
These data show that, while autos generally account for a large frac-
tion of the total number of vehicle miles on unpaved roads, the average ve-
hicle weight mile is substantially larger than that for a car. For the nine
plants, an average weight of approximately 10 tons was found, and over half
of the daily mileage was due to vehicles weighing between 10 and 30 tons.
Although paved roads generally experience a greater volume (approxi-
mately six times, on the average) of traffic than do unpaved roads, it is
important to note that unpaved roads are usually responsible for more SP
emissions. This statement is based on the fact that the leading term for
the AP-42 unpaved road emission factor equation is about 60 times greater
than that for industrial paved roads.6 Because unpaved roads generally are
used by much heavier vehicles, it is apparent that unpaved roads are the
more important source. Furthermore, comparison of the leading terms for the
PM10 equations shows that unpaved roads contribute emissions in this size
range at a level comparable to paved roads.
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SUIWRY TABLE
TOTAL MILES OF PAVED
AND DAILY VEHICLE
MILES OF
BY PUNT I.D. AND TYPE
PLANT 1.0.
1
2
3
4
5
6
7
8
9
MILES OF
UNPAVED
3.62
4.59
1.40
9. SO
1,57
1,4
1.8
10.717
11.51
ROADWAY
PAVED
1.02
0.82
3.35
0.37
0.18
8.2
8.4
14.815
1.97
1
ROADWAY
TRAVEL (V.M.T.)
OF ROADWAY
DAILY
UNPAVED
262.00
115.20
138.50
3675.00'
154.30
384.02
438.60
436.00
1877.30
V.M.T.
PAVED
299.08
4462.40
839.50 ,'
124.00
131.70 :
7750.98
5213.00
9800.00
1105.70
Figure 2-1. Copy of IN-TECH summary data of vehicle miles traveled In
iron and steel industry. '
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SUMMARY TABLE 2
TOTAL DAILY VEHICLE MILES OF TRAVEL FOR UNPAVED ROADWAYS
PLANT 1.0. AUTOS
1 1IB.6
I 43.5
3 641.2
4 25H.33
5 19.10
6 191.6
7 148.70
B 106.2
9 815.7
* K-Mflfi
** SLAfiPOT
DV PLANT l.D. AND VEHICLE
TRUCKS (AXIES)
23456789
18.9 0.24 0.24 4.5
1B.1 30.2 22.2
39.66 132.96
162.96 79.3 47.3 171.6* 1 10.56
27.5 20.3 10.2 48.9
87.5 27.6 B.O 9,5
5.0 128.0 B5.0 19.2
2.9 1.2 5.8 243.5 2.7 0.2
29.60 541.0 5.0
CLASSIFICATION
10 11 EUCLID PLANT
EQUIPMENT
84.0 35.52
1.2
7.0 55.78
61.9*
121.0 443.71
2B.3
60.0
52.7
3.B 2.6 68.10
306.4 85.4
10.4**
TOTAL
262.0
115.20
933.50
3675.00
154.30
384.02
438.60
436.00
1877.3
Figure 2-2, Copy of IN-TECH summary data of iron and steel unpaved road traffic
by vehicle type.
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SUMMARY TABLE 3
AVERAGE VEHICLE HEIGHTS IN THOUSANDS OF
FOR VEHICLES USING WAVED ROADS
BY PLANT 1.0. AMD VEHICLE CLASSIFICATION
PLANT 1.0. AUTOS 2
1
2
3
4
5
6
7
8
9
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
* K-MA6
** SLAG POT
0 Average of
36.0
37.5
31,5
34.5
32.5
27.0
30.6
33.0
33.0
3 4
23.0 47.0
23.0 48.5
23.0 47.5
23.0 47.0
23.0 53.5
23.0 49.7
38.0° 44.0"
37.5°
loaded and unloaded
TRUCKS (AXLES)
56789
49.2
52.5
48.9
46.5 51.5
51.5
52.5
50.6
48.0 52.0 62.0
53.0 56.0
weight
10 11 EUCLID PLANT EQUIPMENT
00.0 36.5
SI. 5 36.0
80.0 28.0
190.00 *
00.0 34.3
33.5
35.0
32.8
72.0 73.0 3i.O
76.0 37.5
145.00 **
Figure 2-3. Copy of IN-TECH summary data of average weights for vehicle types on iron
and steel unpaved roads.
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In summary, most vehicle-generated road dust in the iron and steel in-
dustry appears to be due to unpaved roads, and most of this contribution
arises from medium- to. heavy-duty vehicles. As more roads become paved in
the industry, the relative importance of unpaved road dust emissions may
decline, but the importance of reducing emissions from medium to heavy ve-
hicles on unpaved roads will remain. This is the type of traffic considered
in the field testing program.
2.2 CONTROL MEASURE SELECTION
Historically, the most widely used control measure for unpaved roads,
besides watering, has been Coherex® (a petroleum resin). However, because
of the sharp rise in prices of petroleum-based products over the past de-
cade, the iron and steel industry has expressed interest in less expensive,
alternative chemical controls. These control measures may beeither pet-
roleum resin products similar to Coherex® (such as Resinex 6CP) but with
potentially lower delivery costs, or products of another nature (such as
asphalt emulsions, salts, or adhesives).
In order to assess interest in chemical control of road dust within
the industry, a survey of corporate officials was conducted. Additional
information was obtained during site surveys. The results (based largely
on data from 1984 control programs) are shown in Table 2-1. As can be seen,
petroleum resins represented the most widely used dust suppressants in the
industry at the end of 1984. In fact, only one of the five surveyed corpo-
rations did not use this type of product during 1984. Asphalt emulsions
(e.g., Petro Tac) were the next most widely used suppressant type in the
industry.
To further characterize the changing nature of dust suppressant use in
the iron and steel industry, the survey also contained questions about past
control programs and any plans to evaluate chemicals in the future. The
replies are presented as Table 2-2. In addition to the interest shown in
Dustaside® and Soil Sement, considerable interest in generic petroleum resin
was expressed. These generic formulations were developed at the Mellon
Institute with funding from the American Iron and Steel Institute (AISI).
Upon completion of the survey, five products were selected for field
testing — Coherex®, Petro Tac, Soil Sement, Dustaside®, and Generic 2 (QS).
Based on the results of the survey, these products largely characterized
current and projected practice in the iron and steel industry.
2.3 TEST SITE SELECTION
Because the scope of work for this study required that test sites be
chosen from LTV's Aliquippa, Cleveland and Indiana Harbor works, each of
these plants was surveyed by MRI personnel. Candidate sites were examined
using criteria of: (a) road length and orientation with respect to prevail-
ing winds; (b) traffic mix and rate; (c) upwind/downwind flow obstructions;
(d) general meteorology such as mean wind speed, prevailing direction and
frequency of precipitation; (e) availability of chemical dust suppressants
and application equipment; and (f) proximity to MRI.
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TABLE 2-1. UNPAVED ROAD DUST CONTROL SURVEY RESULTS
00
Company
Armco
Inland
Inland
Inland
LTVe
LTVe
LTVe
LTV
National
Plant
Middletown
Indiana Harbor
Indiana Harbor
Indiana Harbor
AHquippa
Aliquippa
Cleveland
Indiana Harbor
Granite City
Dust suppressants
used
Coherex® (200, 000 }c
Dustaside® (26,500)
Resinex 60® (29,500)
Flambinder (120,000)d
Petro Tac (6,000)c>f
Soil-Sement (18,000)c*f
Water and waste oil
Petro Tac (110,000)
Coherex®
Application
intensity h
(gal/yd^f
0.08/0.08
(20X/12X)
0.3/0.1
(17%/9%)
0.5/0.1
(17%/7 to 9%)
0.5/0.1
(181/10%)
N/A
N/A
N/A
0.5/0.5
(iox/iox)
0.28/0.28
/ 1 ~J*y / 1 T*y \
(I/A/IK)
Application Areas in which
frequency water is used
Once a week to Coal storage, recycle
every 6 months plant, slag process-
ing
Approximately
every 3 weeks
Approximately
twice per week
As needed, based
on visual in-
spection
N/A
N/A
N/A General dust mitiga-
tion
As needed, based
on visual ob-
servation
Once per week to
once per month
(continued)
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TABLE 2-1 (continued)
Application
Dust suppressants intensity . Application
Company Plant used (gal/yd2) frequency
Areas in which
water is used
National Great Lakes Coherex® 0.09 Once per month
(15X)
USS
uss
USS
uss
Fairfield Dustaside® (6.000}9 N/A Quarterly during
the entire year
Gary Resinex 6(^ (500.000)9 N/A Daily rotation
through plant
Geneva Magnesium chloride N/A Every 6 months
Mono Valley Coherex® (50,000)a N/A N/A
Used as a supplement
on roads as needed
(usually once a day)
-
Open unpaved areas
on a weekly rotation
schedule
-
a
b
c
d
e
f
Value in parentheses is gallons delivered in 1984, except as noted.
Initial/follow-up applications. Value in parentheses represents dilution ratio.
1983 value.
Used primarily on storage piles and infrequently for light-duty roads
LTV data obtained during site surveys.
* * i
Estimated value.
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TABLE 2-2. DUST SUPPRESSANTS RECENTLY USED OR CONSIDERED
FOR EVALUATION
Plant
Middletown
Indiana Harbor
Aliquippa
Indiana Harbor
Corporate
Gary
Geneva
Dust
suppressant
Comments
Resinex Full scale program during
1983
Generic Corporate personnel have
expressed interest, but
have no plans to evaluate;
Dustaside® Plant had hoped to purchase
10,000 gal. in 1984, but
business conditions pre- i
vented expenditure. Dust-
aside is preferred for
1985 i
Dustasidefi Considering future evalua-
tion
Soil Sement Considering future evalua-
tion
Coherex® Evaluated in 1982
Resinex® Evaluated in 1982
Generic Corporate personnel have
expressed interest
Dustaside® Currently evaluating
Coherex® Evaluated during 1982-1983;
considered too expensive
10
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On the basis of the above criteria, no site at the
suitable for testing. However, one suitable site was
Aliquippa and Indiana Harbor. At the Aliquippa Works, the ^T
was approximately 1,600 ft long and was oriented southeast »s'
This road was used to haul both slag to processing and refuse
was
3t both
^^IQ ~aad
-CT~hwest.
= "lane-fill-
**£'
The site at Indiana Harbor was the same road that was
earlier study.1 However, the BOF slag haul road had been ;.J;
changed, with the southern half of the road isolated and dev»,t
hauling (approximately two round trips per hour). This road
by the slag processor at the plant, and Coherex® and calcium -
used to control emissions during 1384 and 1985, respectively
part of the road carried a variety of vehicles, ranging from -
ups to scrap trucks and Euclids. This road was maintained b/*
Tac has been applied since 1982.
g an
r^ntially
: -.3 BOF slag
: maintained
" ;*"ide were
~",e rsaaining
"i and pick-
"«, and Petro
site:
In addition, the dust suppressants to be tested were at?-~a*
to
Aliquippa
Indiana Harbor
CoherexS
Generic 2 (QS)
Soil Sement
Coherex®
Petro Tac
Dustaside®
'.;• source of
.van. Note
.-. Tiake inter-
In the decision process, attention was paid to locating a n%a
the dust suppressant and contractors familiar with its app -
that the petroleum resin Coherex® was selected for both sittV
plant comparisons.
2.4 SELECTION OF STUDY DESIGN
In developing a study design to characterize the cent?-.
of unpaved road dust suppressants, both a sampling methodo 1 •„-•
application plan must be chosen. The sampling method must >*
rately characterize the dust emissions, and the control ap-.,
must be developed with attention to possible interference s«
could impact control efficiency determination.
Unpaved road dust emissions are especially difficult .-,
for the following reasons:
1. Both uncontrolled and controlled emission rates h<,,% -,
of temporal variability.
2. Emissions are comprised of a wide range of partic » --ze (includ-
ing coarse particles which deposit immediately adjacent to -,-% source) and
the control efficiency for different size ranges can vary
.^ a C0ntro1
>.'•'„ to accu-
IVlon plan
-s which
11
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The scheme for quantification of emission factors must effectively deal
with these complications to yield source-specific emission data needed to
evaluate the priorities for emission control and the effectiveness of con-
trol measures.
Two basic techniques have been used in quantifying particulate emis-
sions from vehicular traffic on unpaved roads.
1. The upwind/downwjnd7 method involves measurement of concentrations
upwind and downwind of the source, utilizing ground-based samplers (usually
hi-vol samplers) under known meterological conditions. Atmospheric disper-
sion equations are used to back-calculate the emission rate which most
nearly produces the measured concentrations. The Gaussian dispersion equa-
tions art often applied to cases of near-roadway dispersion. However, the
equations generally used were not formulated for such an application.
2. MRI's exposure-profiling8 method involves direct measurement of
the total passage of open dust source emissions immediately downwind of the
source by means of simultaneous multipoint sampling over the effective
cross-section of the open dust source emission plume. This technique uses
a mass balance calculation scheme similar to EPA Method 5 rather than re-
quiring indirect calculation through the application of a generalized atmo-
spheric dispersion model.
The most suitable and accurate technique for quantifying unpaved road
emissions in the iron and steel industry has been shown to be exposure pro-
filing. The method is source-specific and its increased accuracy over the
upwind/downwind method is a result of the fact that emission factor calcula-
tion is based on direct measurement of the variable sought, i.e., mass of
emissions per unit time.
In addition to the above measurement techniques, the study design must
also include a control application plan. Two major types of plans have been
used:
1. Testing is conducted on two or more contiguous road segments. One
segment is left untreated and the others are treated with a separate dust
suppressant.
2. Uncontrolled testing is initially performed on one or more road
segments, generally under worst-case (dry) conditions. Each segment is then
treated with a different chemical; there is no segment left untreated as a
reference. A normalization of emissions is required to allow for differ-
ences in vehicle characteristics during the uncontrolled and controlled
tests because they do not occur simultaneously.
It is important to note that, for the purpose of estimating annual
controlled emissions from unpaved roads, average control efficiency values
based on worst-case (i.e., dry) uncontrolled emission levels are required.
12
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This is true simply because the AP-42 unpaved road predictive equation,6
which is routinely used for inventorying purposes, is based on source tests
conducted under dry conditions. Extrapolation to annual average emissions
estimates is accomplished by assuming that emissions are occurring at the
estimated rate on days without measurable precipitation, and conversely are
absent on days with measurable precipitation. This assumption has never
been verified in a rigorous manner; however, MRI's experience with hundreds
of field tests indicate that it is a reasonable assumption if the source
operates on a fairly "continuous" basis.
The uncontrolled emission factor for a specific unpaved road will in-
crease substantially after a precipitation event as the surface dries. How-
ever, in the absence of data sufficient to describe this growth as a func-
tion of traffic parameters, amount of precipitation, time of day, season,
cloud cover, and other variables, uncontrolled emissions are estimated using
the simple assumption given above. Thus, in order to definitively estimate
emission reductions attributable to a dust suppressant, control efficiency
should be referenced to uncontrolled emissions under dry conditions.
The work plan for this study originally called for field testing to be
conducted over two summers. However, because the study began in June 1984,
no testing was possible until the spring of 1985. Furthermore, it was not
possible to extend the project duration to include the summer of 1986.
Because of the constraint of only one summer available for testing, the pro-
gram was designed to obtain as much useful data as possible during the pe-
riod. In order to achieve this goal, modifications to MRI's prior dust con-
trol evaluation protocol were made.
The first modification was the adoption of a Type 1 control application
plan. The simultaneous testing of both controlled and uncontrolled emis-
sions from the test road under this plan allowed a more flexible set of ac-
ceptability criteria for testing. For example, light rainfall during the
night did not require that the road dry out prior to testing. Thus, adop-
tion of a Type 1 control application plan allowed more tests to be completed.
However, control efficiency would still be referenced to dry conditions.
The second change (again made possible by the Type 1 plan) also allowed
more information to be collected during the field program. This modifica-
tion entailed deploying an "abbreviated" sampling array on days that were
not totally acceptable for exposure profiling. In this case, control effi-
ciency was determined on the basis of net reduction in concentration values
rather than mass emission rates. In addition to providing additional con-
trol performance data, this information proved valuable (a) in evaluating
prior control effectiveness studies for inclusion in model development, and
(b) in assessing the capability of simplified sampling protocols (i.e., not
requiring extensive equipment or labor resources) for estimating control
efficiency.
Thus, the study design for this testing program employed exposure pro-
filing as the primary technique to quantity uncontrolled particulate emis-
sions from vehicular traffic on unpaved roads and to determine the control
13
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performance of the various suppressants. This design not only ill owed the
evaluation of effectiveness for each suppressant but could also provide
information on the seasonal variation of uncontrolled emissions. Finally,
the inclusion of a secondary sampling array had the additional benefits de-
scribed above.
The rest of this section describes the detailed test methodology, in-
cluding air and surface material sampling equipment and techniques, field
and laboratory analysis techniques, and calculation procedures.
2.5 QUALITY ASSURANCE
As part of the QC program for this study, routine audits of sampling
and analysis procedures were performed. The purpose of the audits was to
demonstrate that measurements were made within acceptable control conditions
for particulate source sampling and to assess the source testing data for
precision and accuracy. Examples of items audited include gravimetric anal-
ysis, flow rate calibration, data processing, and emission factor and con-
trol efficiency calculation. Specially designed reporting forms for field
sampling and laboratory analysis data aided in the auditing procedure. Fur-
ther detail on specific sampling and analysis procedures are provided in the
following sections.
2.6 AIR SAMPLING EQUIPMENT AND TECHNIQUE
Exposure profiling, which was the primary air sampling technique in
this study, is based on the isokinetic profiling concept used in conven-
tional source testing. The passage of airborne pollutant immediately down-
wind of the source was measured directly by means of simultaneous multipoint
sampling over the effective cross section of the open dust source plume.
This technique used a mass-balance calculation scheme similar to EPA Method 5
stack testing rather than requiring indirect calculation through the appli-
cation of a generalized atmospheric dispersion model.
In addition, an abbreviated sampling array was deployed when conditions
at the site were not fully suitable for exposure profiling. This secondary
system was designed to provide particulate concentration data (rather than
mass emissions data) for calculation of control efficiency. Use of this
system during periods of marginal wind conditions was designed to provide as
much control efficiency data as possible in the 1 year available to MRI for
testing. The air samplers that were used in the field testing are listed in
Table 2-3. The two sampling arrays are discussed separately below.
14
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TABLE 2-3. AIR SAMPLING EQUIPMENT
Intake height (m)
Location Sampler Full array Abbreviated array
Upwind3 Standard hi-vol/ 2.2 2.2
impactor
Downwi nd
station
Profiling head
Cyclone/impactor
37-mm cassette
1.5
3.0
4.5
6.0
2.2
2.2
-
-
-
-
2.2
2.2
This deployment was modified for testing at Indiana Harbor because
of the potential difference in upwind concentrations. Standard
hi-vol/impactor combinations (each at a 2.2-m height} may be
located upwind of each test strip.
The MRI exposure profiler (developed under EPA Contract No. 68-02-0619)
was used in the "full" array. Each profiler (Figure 2-4) consist of a por-
table tower (4 to 6 m height) supporting an array of sampling heads. During
testing, each sampling head was operated as an isokinetic exposure sampler
directing passage of the flow stream through a settling chamber and then
upward through a standard 20.3- x 25,4-cm (8- x 10-in.) glass fiber filter
positioned horizontally. Sampling intakes were pointed into the wind, and
sampling velocity of each intake was adjusted to match the local mean wind
speed, as determined by 5- to 10-min averages prior to and during the test.
High-volume, parallel-slot cascade impactors (Sierra Instruments, Model
No. 230) with 34-m3/hr (20-cfm) flow controllers were used to measure the
downwind particle size distribution along side the exposure profiler. The
height selected for the downwind samplers was based on an examination of
previous MRI testing.1 3 This height reasonably approximates the point in
the dust plume at which half the mass emissions are above and half below.
The downwind impactor units (as shown in Figure 2-5) were equipped with
Sierra Model No. 230CP cyclone preseparators to remove coarse particles
which otherwise would tend to bound off the glass fiber impaction sub-
strates, causing fine particle measurement bias. To further reduce particle
bounce problems, each substrate was sprayed with stopcock grease solution to
provide a sticky impaction surface. The upwind particle size distribution
was measured using hi-vol/impactor combinations. Experience has shown that
the background size distribution is essential in determining control effi-
ciencies for fine particulate emissions. Each impactor consisted of five
15
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Figure 2-4. MRI exposure profiler.
16
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Seal*'- Inewt
S STAGE CASCAOf
IM? ACTOR
BACK-UP MLTSJt
htCLOER
Figure 2-5. Cyclone preseparator/ciscade impactor combination.
17
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impaction stages (cut-offs for 50% collection are 10.2, 4.2, 2.1, 1.4, and
0.73 umA at 20 ACFM). In order to determine the particle size distributions
at the coarse particle end of the spectrum by microscopy, 37-mm cassette
samplers were deployed at the same locations as the cyclone/impactors.
Throughout each test, wind speed was monitored by warm-wire anemometers
(Kurz Model 465) at two heights, and the vertical wind speed profile was
determined by assuming a logarithmic distribution. An integrating Biram's
vane anemometer was used as a backup system. Horizontal wind direction was
monitored by a wind vane at a single height, and 5- to 10-min averages were
determined electronically prior to and during the test. The sampling in-
takes were adjusted for proper directional orientation based on the average
wind direction.
2.7 EMISSION TESTING PROCEDURE
2.7.1 Preparation of Sampl_e_Co_l 1 ection Media
Particulate samples were collected on Type A slotted glass fiber im-
pactor substrates and on Type AE grade glass fiber filters. As noted in the
last section, all glass fiber cascade impactor substrates were greased to
reduce the problem of particle bounce. The grease solution was prepared by
dissolving 140 g (4.9 oz) of stopcock grease in 1 L (0.26 gal) of reagent
grade toluene. No grease was applied to the borders and backs of the sub-
strates. The substrates were handled, transported, and stored in frames
which protected the greased surfaces.
Prior to the initial weighing, the filters and greased substrates were
equilibrated for 24 hr at constant temperature and humidity in a special
weighing room. During weighing, the balance was checked at frequent inter-
vals with standard (Class S) weights to assure accuracy. The filters and
substrates remained in the same controlled environment for another 24 hr,
after which a second analyst reweighed them as a precision check. If a sub-
strate or filter could not pass audit limits, the entire lot was reweighed.
Ten percent of the substrates and filters taken to the field were used as
blanks. The quality assurance guidelines pertaining to preparation of sam-
ple collection media are presented in Table 2-4.
2.7.2 Pretest Procedures/Evaluation of Sampling Conditions
Prior to equipment deployment, a number of decisions were made as to
the potential for acceptable source testing conditions. These decisions
were based on forecast information obtained from the local U.S. Weather
Service office. Sampling was not planned if there was a high probability
of measurable precipitation.
If conditions were considered acceptable, the sampling equipment was
transported to the site, and deployment was initiated. The deployment pro-
cedure normally took 1 to 2 hr to complete. During this time, the sampling
flow rates were set for the various air sampling instruments. The quality
control guidelines governing this activity are found in Table 2-5.
18
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TABLE 2-4. QUALITY ASSURANCE PROCEDURES FOR SAMPLING MEDIA
Activity
QA check/requirement
Preparation
Conditioning
Weighing
Auditing of weights
Correction for handling
effects
Calibration of balance
Inspect and imprint glass fiber media with
identification numbers.
Equilibrate media for 24 hr in clean con-
trolled room with relative humidity of less
than 50% (variation of less than ± 5%) and
with temperature between 20°C and 25°C
(variation of less than ± 3%).
Weigh hi-vol filters and impactor substrates
to nearest 0.1 mg.
Independently verify final weights of 10% of
hi-vol filters and impactor substrates (at
least four from each batch). Reweigh batch
if weights of any hi-vol filters or impactor
substrates devote by more than ± 2.0 mg and
± 1.0 mg, respectively. For tare weights,
conduct a 100% audit. Reweigh tare weight of
any Imvol filters or impactor substrates
that deviate by more than ± 1.0 mg, and
± 0.5 mg, respectively.
Weigh and handle at least one blank for each
1 to 10 hi-vol filters or impactor sub-
strates of each type for each test.
Balance to be calibrated once per year by
certified manufacturer's representative.
Check prior to each use with laboratory
Class S weights.
19
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TABLE 2-5, QUALITY ASSURANCE PROCEDURES FOR SAMPLING FLOW RATES
Activity QA check/requirement
Calibration
* Cyclone/impactors Calibrate flows in operating ranges using
calibration orifice upon arrival and
every 2 weeks thereafter at each plant
prior to testing.
• Profiler heads Calibrate flows in operating ranges
using calibration orifice upon arrival
and every 2 weeks thereafter at each
regional site prior to testing.
• Orifice and electronic Calibrate against displaced volume test
calibrator meter annually.
Once the source testing equipment was set up and the filters inserted,
air sampling commenced. Information was recorded on specially designed re-
porting forms for quality assurance and included; i
a. Exposure profiler - Start/stop times, wind speed profiles, and
sampler flow rates (5- to 10-min average), and wind direction
relative to the roadway perpendicular (5- to 10-min average),
b. Other samplers - Start/stop times and flow rates, •
c. Traffic count by vehicle type and speed,
d. General meteorology - Wind speed, wind direction, and temperature.
From the information in (a), adjustments could be made to insure iso-
kinetic sampling of both profiler heads (by changing the intake velocity and
orientation) and cyclone preseparators (by changing intake nozzles and ori-
entation). Table 2-6 outlines the pertinent QA procedures.
Sampling time was long enough to provide sufficient particulate mass
and to average over several cycles of the fluctuation in the emission rate
(i.e., vehicle passes on the road). Sampling lasted from 16 min to over
4 hr depending on source activity and control measure (if any). Occasion-
ally, sampling was interrupted due to occurrence of unacceptable meteoro-
logical conditions and then restarted when suitable conditions returned.
Table 2-7 presents the criteria used for suspending or terminating a source
test. :
20
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TABLE 2-6. QUALITY ASSURANCE PROCEDURES FOR SAMPLING EQUIPMENT
Activity
QA check/requirement3
Maintenance
* All samplers
Operation
• Timing
Isokinetic sampling
(profilers only)
Isokinetic sampling
(cyclone/impactors)
Prevention of static
mode deposition
Check motors, gaskets, timers, and flow
measuring devices at each plant prior
to testing.
Start and stop all samplers during time
span not exceeding 1 min.
Adjust sampling intake orientation when-
ever mean wind direction changes by more
than 30 degrees.
Adjust intake velocity whenever mean
wind speed approaching sampler changes
by more than 20%.
Adjust sampling intake orientation when-
ever adjustments are made to the exposure
profiler intake orientation.
Change the cyclone intake nozzle whenever
the mean wind speed approaching the sam-
pler falls outside of the suggested
bounds for that nozzle. This technique
allocates no nozzle for wind speeds rang-
ing from 0-6 mph, and unique nozzles for
each of the wind speed ranges 6-8, 8-11,
11-15, and 15-20 mph.
Cap sampler inlets prior to and immedi-
ately after sampling.
All means refer to 5- to 10-min averages.
21
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TABLE 2-7. CRITERIA FOR SUSPENDING OR TERMINATING AN EXPOSURE
PROFILING TEST
A test may be suspended or terminated if:a
1, Rainfall ensues during equipment setup or when sampling is in progress.
2. Mean wind speed during sampling moves outside the 1.3- to 8.9-m/sec (3-
to 20-rnpn) acceptable range for more than 20% of the sampling time.
3. The angle between mean wind direction and the perpendicular to the path
of the moving point source during sampling exceeds 45 degrees for two
consecutive averaging periods. '.
4, Daylight is insufficient for safe equipment operation,
5. Source condition deviates from predetermined criteria (e.g., occurrence
of truck spill, or accidental water splashing prior to uncontrolled
testing).
a "Mean" denotes a 5- to 10-min average.
2.7,3 SampleHandling and Analysis
To prevent particulate losses, the exposed media were carefully trans-
ferred at the end of each run to protective containers for transportation.
In the field laboratory, exposed filters were placed in individual glassine
envelopes and then into numbered file folders. Impactor substrates were
replaced in the protective frames. Particulate that collected on the in-
terior surfaces of profiler intakes and cyclone preseparators was rinsed
with distilled water into separate sample jars which were then capped and
taped shut.
When exposed substrates and filters (and the associated blanks) were
returned to the MRI laboratory, they were equilibrated under the same con-
ditions as the initial weighing. After reweighing, 10% were audited to
check weighing accuracy.
To determine the sample weight of particulate collected on the interior
surfaces of samplers, the entire wash solution was passed through a 47-mm
(1.8-in.) Buchner-type funnel holding a glass fiber filter under suction.
This water was passed through the Buchner funnel ensuring collection of all
suspended material on the 47-mm filter which was then dried in an oven at
100°C for 24 hr. After drying, the filters were conditioned at constant
temperature and humidity for 24 hr.
22
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All wash filters were weighed with a 100% audit of tared and a
audit of exposed filters. Blank values were determined by washing "clean"
(unexposed) profiler intakes in the field and following the above procedures.
2.7.4 EmissionFactor Calculation Procedure :
To calculate emission rates using the exposure profiling technique, a
conservation of mass approach is used. The passage of airborne particulate
(i.e., the quantity of emissions per unit of source activity) is obtained :
by spatial integration of distributed measurements of exposure (mass/area)
over the effective cross section of the plume. Exposure is the point value
of the flux (mass/area-time) of airborne particulate integrated over the
time of measurement, or equivalently, the net particulate mass passing :
through a unit area normal to the mean wind direction during the test. The ;
steps in the calculation procedure are described below.
Particulate Concentrations— ;
The concentration of particulate matter measured by a sampler is given
by: i
C = 10* J
where; C = particulate concentration (ug/m3)
m = particulate sample weight (mg)
Q = sampler flow rate (nrVmin)
t = duration of sampling (min)
The specific particulate matter concentrations were determined from the
various particulate catches as follows:
Size range Particulate catches
TP Profiler filter + intake or
cyclone + impactor substrates + backup filter
IP Impactor substrates + backup filter
PM10 Impactor substrates * backup filter
FP Impactor substrates +• backup filter
To be consistent with the National Ambient Air Quality Standard for total
suspended particulate (TSP), all concentrations and flow rates were ex-
pressed in standard conditions (25°C and 101 kPa or 77°F and 29.92 in. Hg).
23
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Isokinetic Flow Ratio—
The isokinetic flow ratio (IFR) is the ratio of a directional sampler's
intake air speed to the mean wind speed approaching the sampler. It is
given by:
where: Q = sampler flow rate (mVmin)
a = intake area of sampler (ra2)
U = mean wind speed at height of sampler (m/rain)
This ratio is of interest in the sampling of TP, since isokinetic sampling
assures that particles of all sizes are sampled without bias. In this study,
profilers and cyclone preseparators were the directional samplers used.
Occasionally it is necessary to sample at a superisokinetic flow rate
(IFR > 1.0), to obtain sufficient sample under light wind conditions. Cor-
rection factors for nonisokinetic TP concentrations are based on a relation-
ship developed by Davies.9 The relationship as applied to exposure profil-
ing in the ambient atmosphere is as follows:
5l - JL- d/IFR) "I
Ct ~ IFR " 4Y + 1
where: C = nonisokinetic concentration of particles of diameter d
(L - true concentration of particles of diameter d ;
t ;
Y = inertia! impaction parameter = d2 c (p - p) U/18u D
D - diameter of probe
d = diameter of particle
p = density of air I
u - viscosity of air
p = density of particle
c = Cunningham correction factor I
From Oavies1 equation, it is clear that, for very small d, C = Cfc, and
that, for large values of d, C = Ct/IFR. These observations lead to_the
multiplicative correction factors presented in earlier MRI reports.2 4
24
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A value for the average ratio (R) of nonisokinetic to true
can be found by integrating the product of the particle size di
Davies1 relationship over all possible particle diameters. An
corrected concentration can then be calculated as
and
~nt- f
tOKirmi
Cfl/R
Note that, because the particle-size distribution and the i i,- t'c
corrections are interrelated, isokinetic corrections are of an .s ".
nature. In the present study, isokinetic corrections based on n*- '
method described above were iterated until a convergence -criterj - 1%.
difference between successive TP concentration values was jati-f-°T OT
•»« <• i ,» j I OQB
Using a log-normal distribution of particlj diameter^ tne . . .
cally corrected concentrations obtained by the R-method and by MfqS|°
multiplicative correction factor method differ by less tnan 20% f
values between 0,2 and 1.5, by less than 30% in the IFR ranqt of i TV
and by less than 601 for IFR values between 2.0 and 3.Q.1 b to
n
'
Downwind Particle-Size Distributions —
Particle-size distributions were determined by plotting ran f th
cumulative concentrations measured by each impactor stage to *h<* * h°s °
" " rL° ri ft
^ar, . se da'ca
*--1on for
centration against the 50% cutoff diameters presented ear
were fitted to a lognormal mass size distribution after -
particle bounce, "
The technique used in this study to correct for the s-**a.-t- f +- -,
bounce has been discussed in earlier MRI studies.1'2'3 3 -su^n,,, Partic'e
impactor measurements of airborne particle-size d1stribur-,n •liirf,''
out a cyclone precollector indicate that the cyclone prec'/iecv,!- -
effective in reducing fine particle measurement bias, Hf^»./(j"r'' 1S
the cyclone precollector, a monotonic decrease in collect!;':
on each successive impaction stage is frequently followed v
increase in weight collected on the back-up filter. But, -,
value (0.2 M"i) for the effective cutoff diameter of the 9
filter fits the progression of cutoff diameters for the
the weight collected on the back-up filter should be cons
creasing pattern shown by the weight collected on the imq.
excess particulate on the back-up filter is postulated tc
particles that penetrated the cyclone (with small probab
through the impactor. Although particle bounce is furth
ing impaction substrates, it is not completely eliminates
discussion of techniques used to reduce the effects of p
given elsewhere.1
-,'»'- ag^^ H
«V*:. 0 h as,sum
-icac.--/"r p
-,*.er,iT*
'.*^'-^,, 2 h
'.-.n^-;^"3?5" e
:»y^ V ^j t> C° red
-or;yrs.(| °ou _eas_
A ?ir]
-------
1. The calibrated cutoff diameter for the cyclone preseparator is used
to fix the upper end of the particle-size distribution.
2. The lower end of the particle size distribution is fixed by the
cutoff diameter of the last stage and the measured (or correcteds if neces-
sary) mass fraction collected on the back-up filter. The corrected fraction
collected on the back-up filter is calculated as the average of the fric-
tions measured on the last two stages (Stages 4 and 5).
When a corrected mass is required, excess particulate mass is effec-
tively removed from the back-up filter. However, because no clear procedure
existed for apportioning the excess mass back onto ttie impaction stages, the
size distribution determined for tests with evidence of particle bounce was
constructed using the log-normal assumption and two points—the mass frac-
tion collected in the cyclone and the corrected mass fraction collected on
the back-up filter. The mass fractions associated with the first few impac-
tion stages usually lie very near this line.
Prior examination of particle bounce corrections has shown only negli-
gible changes in size fractions for PM10 and above. Furthermore, FP frac-
tions generally are within a factor of 1.2 when compared to fractions de-
veloped without any correction for particle bounce.10
Particulate Exposures and Profile Integration—
For directional samplers operated isokinetically, total particulate
exposures are calculated by:
E = 10"7 x CUt
where: E ~ total particulate exposure (mg/cm2) !
C = net TP concentration (ug/m3) i
U = approaching wind speed (m/s) ,
t ~ duration of sampling (s) i
The exposure values vary over the height of the plume. If exposure is
integrated over the height of the plume, then the quantity obtained repre-
sents the total passage of airborne particulate matter due to the source
per unit length of the line source. This quantity is called the integrated
exposure A and is found by:
H
A = J* E dh
0 ;
where: A = integrated exposure (m-mg/cm2)
E - particulate exposure (mg/cm2)
h = vertical distance coordinate (m)
H = effective extent of plume above ground (m)
26 !
-------
The effective height of the plume is found by linear extrapolation of the
uppermost net TP concentrations to a value of zero.
Because exposures are measured at discrete heights of the plume, a nu-
merical integration is necessary to determine A. The exposure must equal
zero at the vertical extremes of the profile (i.e., at the ground where the
wind velocity equals zero and at the effective height of the plume where the
net concentration equals zero). However, the maximum TP exposure usually
occurs below a height of 1 m, so that there is a sharp decay in TP exposure
near the ground. To account for this sharp decay, the value of exposure at
the ground level is set equal to the value at a height of 1 m. The integra-
tion is then performed using Simpson's rule.
Total Particulate Emission Factoi—
The emission factor for total airborne particulate generated by vehicu-
lar traffic on a straight road segment expressed in grams of emissions per
vehicle-kilometer-traveled (VKT) is given by:
e = 10*
where: e = total particulate emission factor (g/VKT)
A » integrated exposure (m-mg/cm2)
N = number of good vehicle passes (dimensionless)
Other Emission Factors--
Emission factors for the other particle size ranges were obtained by
multiplying the emission factors by net mass fractions. These mass frac-
tions are found by dividing the net (i.e., downwind minus upwind) concen-
tration for the size range of interest by the net TP concentration.
2-7.5 Control Ef.ficiency_CaT_cu1ati onProcedure
Although controlled and uncontrolled tests were conducted at the same
site, it was necessary to obtain normalized values of emission factors in
order to make meaningful comparisons. This was true simply because the ve-
hicle mix on the test road varied not only from day to day but also during
different shifts on an individual day. Thus, measurement-based ("raw")
emission factors required normalization in order that a change in vehicle
mix was not mistakenly interpreted as part of the efficiency of the control
measure being tested.
The method used in this study to normalize emission factors is based
on MRI's experimentally determined predictive emission factor equation for
uncontrolled unpaved roads and is identical to the process used in earlier
reports.1'2 The emission factors are scaled by:
27
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where; e = normalized value of the emission factor corresponding to
run i *
e. = measured emission factor from run i
S = normalizing value for average vehicle speed
S. * average vehicle speed during run i
!
W = normalizing value for average vehicle weight
W. = average vehicle weight during run i j
w = normalizing value for average number of wheels per vehicle
pass i
w, = average number of wheels per vehicle pass during run i
The control efficiency in percent (c) is then found as:
c = fI - -£ 1 x 100%
S
where: e = normalized emission factor for controlled road
e = geometric mean of normalized emission factors for
uncontrolled roads
This value of efficiency represents the (instantaneous) level of control
over a specific test (and, hence, at a particular time after application).
Another important measure of control performance is average efficiency
defined as:
C(T) = | / c(t) dt
o
28
-------
where: C(T) = average control efficiency during period ending T days after
application (percent)
c(t) = instantaneous control efficiency at t days after application
(percent)
T = time period over which average control efficiency is desired
(days)
This value enables one to determine mass reductions in emissions for thi»
purpose of determining cost-effectiveness,
2.8 AGGREGATE MATERIAL SAMPLING AND ANALYSIS
Samples of the loose road surface were taken from lateral strips or
known area (generally, the width of the road by 30 cm) during the course <>f
this study. These were analyzed for silt (those particles passing a 200-
mesh screen) and moisture contents and to determine road surface loading
values. Detailed steps for collection and analysis of samples for silt ,ind
moisture are given in a previous report,8 An abbreviated discussion is pre-
sented below.
Roadway surface dust samples were collected by sweeping the loose l.i/op
of soil, slag, or crushed rock from the hardpan road base with a broom a(,rj~
dust pan. Sweeping was performed so that the road base was not abraded by
the broom, and so that only the naturally occurring loose dust was colleu,ecj
The sweeping was performed slowly so that dust was not entrained into tlni"
atmosphere.
Once the field sample was obtained, it was prepared for analysis, [i)(3
field sample was split (if necessary) with a riffle to a sample size "
nable to laboratory analysis. The basic procedure for moisture analysis
determination of weight loss upon oven drying. Table 2-8 presents a st%-
by-step procedure for determining moisture content. The basic procedure ;or
silt analysis was mechanical, dry sieving. A step-by-step procedure i-.
given in Table 2-9.
2.9 AUXILIARY EQUIPMENT AND SAMPLES
Provision was made to quantify additional parameters which affect t,-,«.
performance of a control measure applied to unpaved roads. These parameters
include:
1. Intensity of the control application;
2. Number of vehicle passes following application;
3. Vehicle mix of traffic on the controlled road; and
4. Vehicle speed measured by a hand-held radar gun.
29
-------
TABLE 2-8. MOISTURE ANALYSIS PROCEDURES
1. Preheat the oven to approximately 110°C (230°F). Record oven tempera-
ture. |
2. Tare the laboratory sample containers which win be placed in the ;
oven. Tare the containers with the lids on if they have lids. Record
the tare weight(s). Check zero before weighing.
3. Record the make, capacity, smallest division, and accuracy of the
scale.
4. Weigh the laboratory sample in the container(s). Record the combined
weight(s). Check zero before weighing.
5. Place sample in oven and dry overnight.3 j
6. Remove sample container from oven and (a) weigh immediately if uncov-
ered, being careful of the hot container; or (b) place tight-fitting
lid on the container and let cool before weighing. Record the com-
bined sample and container weight(s). Check zero before weighing.
7. Calculate the moisture as the initial weight of the sample and con-
tainer minus the oven-dried weight of the sample and container divided
by the initial weight of the sample alone. Record the value.
8. Calculate the sample weight to be used in the silt analysis as the
oven-dried weight of the sample and container minus the weight of the
container. Record the value.
Dry materials composed of hydrated minerals or organic materials like
coal and certain soils for only 1-1/2 hr. Because of this short dry-
ing time, material dried for only 1-1/2 hr must not be more than
2.5 on (1 in.) deep in the container.
30
-------
TABLE 2-9. SILT ANALYSIS PROCEDURES
1. Select the appropriate 8-in. diameter, 2-in, deep sieve sizes. Recom-
mended U.S. Standard Series sizes are: 3/8 in., No. 4, No. 20,
No. 40, No. 100, No. 140, No. 200, and a pan. Comparable Tyler Series
sizes can also be utilized. The No. 20 and the No. 200 are mandatory.
The others can be varied if the recommended sieves are not available
or if buildup on one particular sieve during sieving indicates that an
intermediate sieve should be inserted.
2. Obtain a mechanical sieving device such as a vibratory shaker or a
Roto-Tap (without the tapping function).
3. Clean the sieves with compressed air and/or a soft brush. Material
lodged in the sieve openings or adhering to the sides of the sieve
should be removed (if possible) without handling the screen roughly.
4. Obtain a scale (capacity of at least 1,600 g) and record make, capac-
ity, smallest division, date of last calibration, and accuracy.
5. Tare sieves and pan. Check the zero before every weighing. Record
weights.
6. After nesting the sieves in decreasing order with the pan at the bot-
tom, dump dried laboratory sample (probably immediately after moisture
analysis) into the top sieve. The sample should weigh between 800 and
1,600 g (1.8 and 3.5 Ib), Brush fine material adhering to the sides
of the container into the top sieve and cover the top sieve with a
special lid normally purchased with the pan.
7. Place nested sieves into the mechanical device and sieve for 10 min.
Remove pan containing minus No. 200 and weigh. Repeat the sieving
in IQ-min intervals until the difference between two successive pan
sample weighings (where the tare of the pan has been subtracted) is
less than 3.0%. Do not sieve longer than 40 min.
8. Weigh each sieve and its contents and record the weight. Check the
zero before every weighing.
9. Collect the laboratory sample and place the sample in a separate con-
tainer if further analysis is expected.
10. Calculate the percent of mass less than the 200-mesh screen (75 urn).
This is the silt content.
This amount will vary for finer textured materials; 100 to 300 g may
be sufficient when 90% of the sample passes a No. 8 (2.36-mm) sieve.
31
-------
Because the efficiency associated with a control measure is only directly
applicable to a particular dilution ratio and application intensity, it is
important that these variables be quantified.
By either working closely with plant personnel or actually contracting
the work, MRI was able to directly oversee the mixing and application of the
solution. To measure the application intensity, tared sampling pans were
placed at various locations on the road surface prior to application. These
pans were detp enough (- 15 cm) that material splashing on the bottom did
not escape.
After the control was applied, the sample pans were reweighed and the
density of the solution determined. The application intensity measured by
each pan is given by:
~ m..
P A
where: a = application intensity (volume/area)
m,- = final weight of the pan and solution (mass)
nL ~ tare weight of the pan (mass)
p = weight density of solution (mass/volume)
A = area of the pan (area)
Application intensities measured by each pan were examined for any spatial
variation.
In order to define decay as a function of traffic rate as well as time,
pneumatic tube axle counters were deployed at the site after control appli-
cation. In addition to vehicle counts during testing, independent counts
determining the distribution of vehicles by number of axles were taken dur-
ing each shift at the plant. This information was used to convert axle
counts into the number of vehicle passes.
32
-------
SECTION 3.0
CHRONOLOGY OF THE FIELD TESTING PROGRAM
AND TEST RESULTS
The preceding section described the study design and testing plan for
this field program; however, several unanticipated events necessitated that
portions of the original plans be altered. This section discusses the pro-
gram as it evolved in response to these events. In addition, field results
are presented and discussed.
3.1 MODIFICATIONS TO TEST PLAN
MRI field testing personnel arrived at LTV's Indiana Harbor Works on
Monday, April 29, 1985. During the next few days, several conversations
between MRI and Preventive Maintenance Corportion (PMC) representatives took
place at the plant. (At the time, the Indiana Harbor Works was separately
evaluating PMC s Dustaside® at various locations In the plant.) After sev-
eral telephone calls by both parties to the project officer, PMC requested
that Dustaside® not be included in the field evaluation.
Further discussions with the plant's environmental staff revealed in-
terest in the calcium chloride dust suppressant (Liquidow®) used by the BOF
slag processor. Much of this interest was due to the low cost and the fact
that the supplier applied the material, thus eliminating certain costs in
common dust control plans. Arrangements were made to obtain calcium chlo-
ride as the third chemical for evaluation at Indiana Harbor.
On May 17, 1985, with MRI personnel present at Indiana Harbor, LTV an-
nounced that the Aliquippa Works would be closed. Subsequent discussion
with personnel at both plants revealed that, although August 17 would be the
target date for cessation of many operations, steel making would end on
June 28. Thus, only negligible traffic would be present on the slag haul
road at Aliquippa after June 1981.
In a meeting with the EPA technical monitor in Kansas City on May 23,
several options were discussed in order to continue the field efforts in
the most productive manner possible. Both MRI and the technical monitor
agreed that testing at the Kansas City Works of Armco, Inc., would be pre-
ferred for the following reasons:
1. Little time would be lost in conducting a new site survey because
this road had been used in an earlier study1
33
-------
2. Travel costs would be reduced
3. The road at Armco was long enough to permit five contiguous test
sections so that all five chemicals could be evaluated on the same
road
4. MRI would be able largely to control the service environment of
the road in order to simulate typical industry traffic character-
istics
Drums of Cohere)® and Generic which had earlier been delivered to the
Aliquippa Works were shipped to Kansas City. Furthermore, arrangements were
made to obtain Petro Tac and Soil Sement in small lots and to have calcium
chloride available for application in Kansas City.
3.2 SOURCE DESCRIPTION
The following tests were performed at two iron and steel plants - LTV's
Indiana Harbor Works (designated hereafter as plant AP) and Armco's Kansas
City Works (plant AQ):
Plant AP
Five tests of uncontrolled emissions
Six tests on a road treated with Cohere*®
Six tests on a road treated with Petro Tac
Three tests on a road treated with calcium chloride (Liquidow®)
Plant AQ
Two tests of uncontrolled emissions
Nine tests on a road treated with Coherex® ;
Eleven tests on a road treated with Soil Sement
Eleven tests on a road treated with Generic 2 (QS)
Five tests on a road treated with Petro Tac ;
Six tests on a road treated with Liquidow©
T^i
Maps of the test sites at plants AP and AQ are shown as Figures; 3-1
and 3-2, respectively. Areas shown as buffers at the AP test site were
treated with either calcium chloride or Petro Tac; in addition, open areas
in the scrap storage yard south of the road were treated with Petro Tac to
reduce potential upwind impacts.
Several difficulties in controlling traffic patterns at the AP site
were encountered. For example, scrap steel and processed slag haulers often
entered the test area at the southwest corner of the pipe mill, traveled be-
tween the slag piles and the mill and made U- turns onto the scale. The
plant erected a barricade using barrels and plastic streamers at the mill's
southwest corner; this, however, did not prove effective. Later, barricades
made by piling slag proved to be much more effective.
34
-------
UJ
en
too
\±
o
N
I
SEAMLESS PIPE MILL.
/n*t fdanr canr/To/ {Rtrro
300
500
400
i I
—j CattTGOtLED TEST
Cti/oric/e
JSOm
Figure 3-1. Test site at plant AP,
-------
N
150m
Figure 3-2. Test site at plant AQ.
-------
The AQ test site, on the other hand, allowed MRI much greater control
of the service environment for the test sections. This road was located in
a remote corner of the plant and experienced only sporadic traffic related
to plant security and maintenance of a levee to the southeast. Arrangements
were made with the plant's slag processor to fill and grade turnarounds at
the east and west ends of the road to accomodate captive traffic supplied
by MRI. The captive traffic consisted of a 10-ton, 6-wheel dump truck pe-
riodically supplemented with an R-25 Euclid. All travel was confined to the
north side of the road; this not only accelerated the decay rate under traf-
fic but also decreased the road length required for a test section.
The AQ test road experienced average traffic volumes of 110 and 130 ve-
hicle passes (VP) per working day between September 3 and October 3, 1985,
and between October 21 and the end of testing, respectively. Because vehi-
cles were restricted to one side of the road, these volumes represent 220 to
260 passes per day for two-way traffic. These two-way equivalent volumes
closely approximate the mean value of 220 VP/day obtained from the data in
Section 2.1. All traffic occurred at a nominal speed of 24 kph (15 mph).
The east and west ends of the road (including the turnarounds) were
treated as buffer areas. Roughly 25% of the total load of each chemical
(excluding calcium chloride which was applied by the delivery truck) was !
used on the test section and the remainder applied to the buffer areas
(Generic and Soil Sement at the east end, Petro Tac and Coherex® at the
west). Buffers between individual test sections were formed by overlapping
chemical applications (excluding calcium chloride) by approximately 10 m.
These buffers were routinely swept to reduce track-on and were periodically
retreated using a new dust suppressant supplied by Syn Tech Products Corpo-
ration.
3.3 CONTROL APPLICATIONS
MRI supervised the application of all chemicals (both test sections and
buffer areas) during this field program. Measurements of application rate
and density were taken whenever test sections were treated. Tables 3-1 and
3-2 present the application parameters for plants AP and AQ, respectively.
The test sections were characterized in terms of silt content and total
loading prior to treatment. Summary data are given below:
Mean Values of !
Pre-Application Surface Characteristics
Plant AP Plant AQ
Total loading (g/m2) 9,400 1,200
Silt content (%) 8.0 13.9
Silt loading (g/m2) 750 170
37
-------
TABLE 3-1. APPLICATION PARAMETERS - PLANT AP
Chemical
Petro Tac
Coherex®
Calcium chloride
Test
strip
1
2
3
Dilution Application intensity, L/ra2 (gal/yd2)
ratio 5/1785
5:1 2.0 (0,44)
5:1 2.5 (0.56)
a 1.1 (0.25)
S/6/85
1,0 (0.23)
1.0 (0.22)
0.86 (0.19)
6/14/85
3.8 (0.83)
4.5 (1.0)
1.7 (0.38)
7/2/85
-
.
1.5 (0.34)
As received (38% solution).
Application of 6/14/85 largely washed off due to heavy rains shortly
after completion. Delays in scheduling distribution truck and un-
favorable meterological conditions postponed reapplication until 7/2/85.
TABLE 3-2. APPLICATION PARAMETERS - PLANT AQ
Chemical
Calcium chl
Coherex®
Soil Sement
Generic
Petro Tac
Test
strip
oride 5
4
3
2
1
Dilution Application
ratio
a
5:1
5:1
5:1
5:1
7/26/85
1.1
0.95
0.72
0.63
0.95
(0-
(0.
(0.
(0.
(0.
24)
21)
16)
14)
21)
intensity, L/m2 (gal /yd2)
9/3/85
1.3
1.6
2.0
2.1
1.6
(0
(0
(0.
(0.
(0.
.29)
.36)
44)
46)
35)
10/17-21/85
2.3
7,2
1.3
7.7
7.7
(0.
(1.
(0.
(1,
(1.
51)
6)
28)b
70)c
70)
a As recei
ved (38% sol
ution).
Kir
f-*r-¥t
T2sn4"
214- "1 ua
c Dilution ratio of 12:1, following recommendations of Mellon Institute.
38
-------
Figure 3-3 shows the variation of these surfaces parameters over the two
test roads.
3.4 RESULTS OF THE EXPOSURE PROFILING TESTS
Sixty-four exposure profiling tests of unpaved road dust emissions were
conducted during the course of this study. Site parameters associated with
these tests are presented in Tables 3-3 and 3-4. Several remarks about
these tables are in order.
First, each test is identified by a run number composed of a plant pre-
fix, a sequential identification number and finally, a road section suffix.;
The suffix serves to identify the type of control applied to the road seg-
ment. The letter "U" indicates an uncontrolled surface. In addition, each
suppressant has been assigned a code letter as follows:
Test section
Code letter Suppressant Plant AP PI ant AQ
P Petro Tac 1 1
G Generic - 2
S Soil Sement - 3
X Coherex® 2 4
C Calcium chloride 3 5
Code letters are used hereto discourage selective citation of test re-
sults. Because the selection of a dust control product necessarily entails
evaluation of both performance characteristics and cost considerations (such
as capital equipment costs), no individual table of results taken from this
report can provide all the information required. Therefore, the reader is
strongly cautioned against taking any results out of context. The report as
a whole provides a better basis for decisions than does any single data set.
Secondly, four tests (AP-1) wert performed using the abbreviated sam-
pling array discussed in Section 2.0. In these tests, upwind concentrations
were larger than three of the four downwind values. Much of the difficulty
associated with the abbreviated array at plant AP stemmed from the very ir-
regular flow patterns caused by surrounding structures. No further attempts
were made to use the abbreviated array at this plant. Although plant AQ
would have proved more amenable to the abbreviated sampling array, the ori-i
entation of the road to prevailing wind direction and the site's proximity
to MRI's main laboratories made this array unnecessary. ••
No tests of calcium chloride took place at plant AP after run AP-3.
The shorter lifetime of calcium chloride compared to the other two chemicals
made it difficult to avoid possible contamination of adjacent sections. ;
39
-------
0
TOTAL
j
?
/
as
o
0 I-
3/t.T CONTENT
i 2 3
AP T£ST
AQ T£ST
Figure 3-3. Variation of surface material properties (before control) over
test sections (see text for mean values).
40
-------
TABLE 3-3, TEST SITE PARAMETERS - PLANT AP
- „_. " "' "" —
Ambient .
tempera tt
Run Date (°C)
API PJJ 5/07/85 21C
X.
Cb
UD
AP2 P 5/09/85 21C
X
C
U
APS P 5/10/85 21C
X
C
U
APS P 8/17/86 23
X
AP6 P 8/19/85 24
X
U
AP7 P 8/23/85 22
X
U
air Mean
jre wind speed*1
(8./S)
-
-
-
—
4.8
3.4
1.9
1.9
4.9
3.8
3.8
2.8
1.2
1.7
0.91
1.6
1.6
0.41
0.72
0.72
(mph)
11
7.6
4.2
4.2
11
8.5
8.5
6.2
2.6
3.9
2.0
3.7
3.7
0.92
1.6
1.6
les,l
start Duration
time (min)
1227
1227
1227
1227
903
903
903
904
850
850
850
850
945
943
1437
1440
1450
825
821
823
10
27
76
37
128
128
128
127
119
119
119
119
84
82
59
56
46
104
109
87
Niimliiir of
VL'tllt: lu
passes
8
16
48
19
68
68
65
8
50
50
50
10
34
34
51
51
51
87
90
85
tfujilc ju_ wulylil
27
26
26
29
24.6
24.6
25.5
30.0
26.4
26.4
26.4
33.7
25.5
25.5
23.7
23.7
23,7
23.7
23.7
22.8
30
28
29
32
27
27
28
33
29
29
29
37
28
28
26
26
26
26
26
25
Avurti(|i> Nil,
J»t WllUltlb
15.5
14.9
15.4
15.0
12.3
12.3
11.8
7.0
7.08
7.08
7.08
5.20
13.9
13.9
17.4
17.4
17.4
13.5
13.4
13.4
Measured at 4.5 m height
Abbreviated array.
Estimated value.
just prior
to, during,
and immediately after
tests.
-------
TABLE 3-4. TEST SITE PARAMETERS - PLANT AQ
Ambient air Mean
temperature wind speed
Run Date (°C)
AQ1 U 9/16/85 28
G
S
X '
AQ2 U 9/16/85 28
G
S
X '
AQ3 P 9/17/85 24C
G
S
X
AQ4 G 9/17/85 24C
S
X '
c
AQ5 P 10/02/85 17
G
S
c ••
AQ6 P 10/02/85 24C
------ G - - .. _
S
C
(m/s)
3.8
3.9
5.1
4.0
4.0
4.0
4.9
4.5
5.2
5.8
2.6
2.2
(mph)
8.4
8.7
11
9.0
9.0
9.0
11
10
12
13
5.9
5.0d
Test
start
time
1025
1024
1012
1015
1241
1229
1226
1229
954
943
945
948
1630
1531
1720
1535
1055
1208
1045
1054
1537
1436
1436
1436
Duration
(MI in)
64
66
75
75
69
82
85
82
105
52
50
47
22
28
22
33
21
20
29
20
18
28
23
23
Number
'of vehicle
passes
50
50
50
50
68
68
68
68
76
19
19
19
50
50
50
50
34
34
34
34
44
36
36
36
Average
vehicle weight
(Kg)
9.1
9.1
9.1
9.1
8.9
8.9
8,9
8.9
8,8
S.5
8.7
8.7
22
22
22
22
22
22
22
22
22
22
22
22
(ton)
10
10
10
• 10
9.8
9.8
S.8
i.8
9.7
9.3
9.6
9.6
24
24
24
24
24
24
24
24
24
24
24
24
Average No.
of wheels
6.0
6.0
6.0
6,0
5.9
5.9
5.9
5.9
5.9
5.8
5.9
5.9
6.0
6.0
6.0
6.0
5.9
5.9
5.9
5.9
6.0
6.0
6.0
6.0
(continued)
-------
TABLE 3-4 (continued)
4s.
Co
Ambient air Mean Test Number Average
temperature wind speed start Duration of vehicle vehicle weight Average No.
Run
AQ7
"j
u
AP8
AP9
AP10
»
Apll
P
6
S
x-
P
G
S
X.1
G
S
X1-'
C'
G
S
X
c
G
S
X
c
Date (°C)
10/03/65 18
10/03/8& 21
10/25/85 18
11/05/85 16
11/05/85 13
(m/s) (mphj time
2.9 6.5d 1149
1245
1150
1150
2.2 5.0d 1558
1520
1518
1518
2.9 6.5 1117
1117
1143
1124
2.9 6.6 954
954
954
1000
3.9 8.7 1349
1349
1349
1348
(mm)
30
25
28
28
22
16
17
17
110
110
62
267
138
134
129
133
127
127
130
130
passes
50
48
50
50
36
34
34
34
125
125
79
125
200
200
200
200
250
250
250
250
(Hg)
22
22
22
22
22
22
22
22
9.
9.
9.
9.
6.
6.
8.
6.
5.
5.
5.
5.
1
1
1
1
9
9
9
9
9
9
9
9
(ton) of wheels
24
24
24
24
24
24
24
24
10
10
10
10
7.6
7.6
7.6
7.6
6.5
6.5
6.5
6.5
6.
6.
6.
6.
6.
6.
6.
6.
6.
6.
6.
6.
5.
5.
5.
5.
5.
5.
5.
5.
0
0
0
0
0
0
0
0
0
0
0
0
3
3
3
3
0
0
0
0
a 4.
5-m
During
equivalent prior to,
profiler ooeration.
during and after tests,
except as
noted.
3 * ~ • — -_...._- _ „, — ___
c Estimated value.
d 3.0-m
height.
-------
.C
O
20 —
15 —
Ul 0
0
IO
O ~"~
O
Jw/y Au^os"} September
Figure 3-4. Cumulative rainfaU and
of
at pUr.i
-------
Furthermore, as noted in Table 3-1, the treatment of June 14 was largely
washed off by heavy rains a few hours after application. Problems in sched-
uling the distributor truck and unfavorable meteorological forecasts delayed
reapplication. It appeared that at least the section treated with Colierex®
was contaminated during the 2 weeks between applications. The decision to
abandon the C test strip and to treat both this strip and the surrounding
buffers as part of the plant's Petro Tac rotation was made in conjunction
with the project officer.
Further difficulties were encountered with standing water and rutting
of the road at plant AP. The first 2 weeks of August produced 100% of nor-
mal precipitation for the month. The south sides of the X test section and
of the buffer were found to be badly rutted once the water was removed. To
ensure safe vehicle operation and to reduce spillage, these areas were
slagged and treated with Petro Tac. All subsequent tests restricted traffic
to the north side of the road.
During the same period, the uncontrolled section at plant AP also con-
tained standing water. After drying, the surface resembled caked mud and
was not representative of the road prior to application of controls.
Finally, testing at plant AQ was hampered by one of the wettest falls
on record. Figure 3-4 compares the 1985 precipitation record with the
cliiatological record; in addition, important events are noted along the
time line. During the four months after the initial control applications,
recorded rainfall represented 224, 160, 306, and 263% of normal. The,heavy
rains of mid-October caused flooding in areas surrounding the test site and
all sampling equipment was moved to higher ground. Standing water during
this period caused severe rutting in the P test section. Regrading was nec-
essary for safe vehicle operation; this, however, required that the surface
be abandoned for further testing. After regrading, this segment was heavily
retreated periodically as were the other buffer areas. !
Tables 3-5 and 3-6 compare, for each run, raw concentration values both
upwind and downwind of the test road. It should be noted that direct com-
parison of TP concentrations measured by the profiler and cyclone/impactor
samplers is hindered by the fact that the latter units were often operated
for a longer time period to obtain adequate sample mass on each substrate.
Table 3-7 and 3-8 gives measured wind speeds and isokinetic flow ratios
for the profile or sampling heads. These values, in conduction with the
aerodynamic particle size data presented in Tables 3-9 and 3-10, were used
to determine isokinetically corrected TP concentrations as needed, using the
procedure described in Section 2.7. Note that some tests at plant AP are
characterized by wind speed profiles which decrease with height. It is be-
lieved that this is the result of irregular flow patterns due to surrounding
obstructions.
44
-------
TABLE 3-5, REPRESENTATIVE CONCENTRATIONS3 (Mg/m3) - PLANT AP
•••••••IMHi
Run
AP2
AP3
APS
AP6
AP7
P
P
X
C
u
P
X
C
u
P
X
P
X
u
P
X
u
Upwind hi-vol
5S2
1,660
1,020
609
357
537
371
796
705
487
2,200
2,590
1,190
284
209
535
Downwind cyclone
421
483
787
1,130
277
364
503
1,040
3,830
6,730
7,330
12,400
2,080
12,800
5,610
12,800
Downwind profiler
721 ;
810^
736C
1,520
545
554 i
508
1,400 1
2,890 1
5,250 ''
5s400d
9,740
2,830 ]
6,160 I
3,730 i
7,330 !
a At 2.2-m height, except as noted.
b Interpolated from profiler heads nearest 2.2-m height.
c 1,5-m value.
d Estimated value.
46
-------
TABLE 3-6, REPRESENTATIVE TP CONCENTRATIONS3 (»g/m>) -
4Q
Run Upwind hi-vol Downwind cyclone
AQ1 U
G
5
X
AQ2 U
G
S
X
AQ3 P
G
S
X
AQ4 G
S
X
C
AQ5 P
G
S
C
AQ6 P
G
S
C
AQ7 P
G
S
X
AQ8 P
G
S
X
AQ9 G
S
X
C
AQ10 G
S
X
C
AQ11 G
S
X
C
54
54
54
54
54
54
54
54
121
121
121
121
121
121
121
121
190
190
190
190
190
190
190
ISO
112
112
112
112
112
112
112
112
32
32
32
32
63
63
63
63
63
63
63 '
63
a At 2.2-m height, except as
Interpolated
from profiler
2,340
1,330
741
2,300
2,340
1,330
741
2,300
709
i93
567
1,170
4,730
3,480
6,610
5,320
§,790
3, §70
3,560
7,860
6,860
5,970
9,810
13,500
5,720
2,980
2,770
8,750
3,270
4,190
3,090
3,210
670
126
278
171
863
301
330
230
863
301
330
227
noted.
heads nearest 2.2-m
Downwind profile^
2,810
3,020
1,280
5,330
3,050
2,320
647
2,300C
1,330
453
299
318
22,600
7,080
14,800
8,480
14,100
6,040
6,780
23.600
22,800
9,840
7,030
22,800
3,990
4,560
6,130
12,700
5,900
5,300
6,390
15,800
856
230
181
121
1,170 ,
40 7d
343
453
1,600
452
387
378
height..
c Estimated value.
3.0-m value.
47
-------
TABLE 3-7. ISOKINETIC CORRECTION PARAMETERS - PLANT AP
Wjnd speed Profiler
1.5 m
Run
AP2
AP3
APS
APS
AP7
P
X
C
u
P
X
C
u
P
X
P
X
u
P
X
u
(cm/s)
167
149
130
158
305
283
283
266
168
165
167
173
168
48
67
63
(fpm)
328
294
256
312
600
558
558
524
330
324
328
341
330
94
132
124
4.5 m
(cm/s)
486
339
206
197
481
372
395
268
117
179
90
160
149
40
73
68
(fpm)
957
667
406a
387a
947
732
778*
528a
231
352
178
315
294
78
143
134
isokinetic flow ratios
1.5 m
1.59
1.40
1.01
1.01
1.02
1.00
0.86
1.00
0,57
0.86
1.02
0.93
0.95
4.34
1.73
1.53
3 m
0.85
-
-
0.96
0.99
1.22
1.23
0.99
0.44
0.92
1.29
1.00
1.02
2.42
1.84
1.46
4-5 m
0.68
1.13
-
1.04
1.42
-
0.74
0.93
1.89
1.06
1.11
2.80
1.56
1.70
6 m
0.74
1.03
2.09
1.03
1.03
1.36
1.44
1.15
0.78
0.93
3.51
i.ii
1.04
3.83
1.41
1.41
6.0-m value.
48
-------
TABLE 3-8. ISQK1NITIC CORRECTION PARAMETliS - PLANT AQ
Wind speed
Run
AQ1
AQ2
AQ3
AQ4
AQ5
AQ6
AQ?
AQS
AQ9
AQ10
AQ11
U
G
S
X
U
G
S
X
P
G
S
X
G
S
X
c
P
G
S
c
P
G
S
c
P
G
S
X
P
G
S
X
G
S
X
C
G
S
X
C
G
S
X
c
1.5
(CBI/S)
115
115
113
114-
235
228
225
225
252
277
281
287
247
144
250
280
215
206
200
222
104
171
189
189
182
192
175-
175
148
143
142
142
163
163
186
16S
109
110
111
110
178
178
175
177
m
(fpra)
226
226
222
224
463
448
443
443
496
546
554
565
487
283
493
551
424
405
394
438
205
337
372
372
359
378
345
345
291
282
279
279
321
321
366
326
215
216
218
217
350
350
344
348
4.
(cmh)
458
4S8
440
445
389
401
384
386
504
465
468
473
494
443
497
559
356
233
348
374
190
313
345
345
331
367
321
321
271
247
241
241
287
287
326
286
292
294
294
295
379
377
383
378
5 f»
(fpm)
902
901
866
876
766
789
756
759
992
915
922
932
973
872
978
1100
700
458
686
737
374
617
680
680
652
722
632
632
533
486
475
475
565
565
641
563
574
578
578
582
747
742
763
744
Profiler
isokinetic
1.5 m
1.01
1.01
1.03
1.02
1.06
0.97
0.98
-
1.16
0.66
0.72
1.81
1.54
2. OS
1.28
1.44
0.76
0.95
0.75
0.74
2.11
1.28
1.06
1.16
0.89
1.03
0.91
0.91
1.48
1.53
1.42
1.29
0.99
0.99
1.01
1.04
1.03
1.04
1.21
1.02
1.03
1.03
1.05
1.03
3 m
0.67
0.67
0.70
0.68
0.91
0.91
0.91
-
O.il
0.79
0.78
m
1.07
1.13
0.88
1.00
0.82
0,97
0.69
0.78
1.62
0.98
0.70
0.70
0.88
1.02
0.90
0.90
1.14
1.38
1.36
1.36
0.94
0.94
0.95
1.01
1.01
1.09
0.97
1.00
0.46
1.04
1,01
1.01
flow ratio
4.5 »
0.63
0.63
0,67
0.65
0.97
0.87
0.96
0.9S
0.80
-
0.36
0.85
1.05
1.05
0.74
0.98
0.86
0.98
0.71
0.82
1.25
0.76
0.69
0.69
0.86
0.79
0.90
0.90
1.01
1.27
1.34
1.34
0.94
0,94
0.98
J.OO
1,01
0.83
1.12
-
1.02
1.03
1.04
1.02
6 a
0.60
0.60
0.64
0.6?
1.01
-
1.00
1.00
0.87
-
0.38
0.88
0.9-3
0.98
0.80
0.91
0.86
1.00
0.75
0.8?
1.20
0.71
0-3
-------
TABLE 3-9. AERODYNAMIC PARTICLE SIZE DATA - PLANT AP
Percent less
Run
AP2
AP3
APS
AP6
AP7
P
X
C
U
P
X
C
U
P
X
P
X
U
P
X
U
% < 15
UW
88
85
36
82
84
84
84
87
67
75
72
76
. 71
79
74
76
ym
OW
19
32
34
42
31
31
40
42
28
24
28
31
42
11
14
18
%
UW
83
80
81
76
79
79
78
83
57
70
64
S3
62
73
68
63
than stated size !
< 10 ym
DW
15
25
28
34
26
26
35
34
22
19
22
24
31
9
12
13
% <
UW
60
61
57
53
56
56
51
62
22
50
32
36
29
54
50
26
: 2.5 MBI
DW
6
i 10
11
10
14
ill
16
:i3
; 8
; 7
; 6
i 8
7
'• 3
4
• 4
a UW denotes upwind, DW downwind.
50
-------
TABLE 3-10. AERODYNAMIC PARTICLE SIZE DATA - PLANT AQ
Run
AQ1
AP2
AQ3
AQ4
AQ5
AQ6
AQ7
AQ8
AQ9
AQ10
AQ11
U
G
S
X
U
6
S
X
P
G
S
X
G
S
X
c
p
G
S
c
p
G
S
C
p
6
5
X
P
G
S
X
G
S
X
c
G
S
X
c
G
S
X
c
i <
UW
93
93
87
87
96
96
85
85
100
86
86
Percent
15 urn
ow
24
26
18
12
24
26
18
12
29
20
20
9
19
17
10
8
21
22
20
24
17
17
18
22
16
21
15
20
2S
27
24
2S
34
38
28
31
34
38
28
32
34
38
28
32
less than
% < 10
UW
86
86
62
62
88
88
58
S8
100
75
75
stated sizea
Mm % < 2.5 t
DW UW
17 66
21
14
10
17 66
21
14
10
26 45
16
16
7
15 45
13
7
7
17 85
18
16
li
12 85
12
14
16
12 48
16
10
14
18 44
21
18
19
26 100
29
20
22
26 42
29
20
24
26 42
29
20
24
in
ow
4
Q
5
3
4
9
5
3
16
6
6
2
6
4
2
2
7
6
6
7
3
3
5
6
4
c
2
5
S
3
4
7
7
6
S
5
7
5
5
7
7
6
5
7
3 UW denotes upwind, DW downwind.
51
-------
Individual point values of isokinetic TP exposure (net mass per sam-
pling intake area) within the open dust source plume are presented in Tables
3-11 and 3-12. These are values integrated over the height of the plume to
develop emission factors, is discussed in Section 2.0.
Tables 3-13 and 3-14 present size-specific emission factors for plants
AP and AQ, respectively. Also shown in these tables are surface aggregate
material properties. These values have been normalized using the procedure
described in Section 2.7.
Finally, mean uncontrolled normalized emission factors for the two
plants were obtained in order to determine control efficiency; '
Mean Normalized Uncontrolled Emission <
Factors (g/VKT)a
Parti cle size fraction
Plant TP IP PM10 FP
AQC
8,850
3,690
1,860
821
1,510
561,
186
101
Normalized to 25 Mg (28 tons) and
12 wheels for plant AP, and 9.1 Mg •:
(10 tons) and 6 wheels for plant AQ. 1
All tests were conducted with a
nominal 24-kph (15-mph) average ve- ;j
hide speed.
Only runs AP-2 and -3 used because
later tests were characterized by
muddy and caked surface.
c Corrected to average silt content of ;
treated sections before application.
Several remarks about these mean values are in order. First, it was
necessary to correct the AQ uncontrolled emissions to reflect the silt con-
tent of the treated sections before application. ;
52
-------
TABLE 3-11. PLUME SAMPLING DATA - PLANT AP
Run
AP2
APS
APS
AP6
AP7
P
X
C
U
P
X
C
U
P
X
P
X
U
P
X
U
Safflpl
1.5 m
37
36
24
27
29
26
23
25
17
25
30
28
28
35
20
17
inq rate (m3/hr)
3 m
57
-
-
30
38
38
38
25
11
28
29
29
29
19
23
17
4.5 m
60
70
-
—
46
49
-
-
IS
29
30
30
29
20
20
20
6 ffl
77
76
75
36
50
52
52
28
13
30
32
30
32
24
18
17
Net TP exposure3 (mg/cm2
1,5 m
0.457
0
0
1.33
0.527
0.271
0.510
1.34
2.15
4.02
3,13b
4.47
0.617
1.54
1.83
2.29
3 ID
0
0
0
0.952
0.380
0
0.0194
0.955
1.27
3.87
2.23.
3.66b
0.877
1.84
1.29
2.08
4.5 m
0
0
0
(0.809)
0.0647
0
(0.00970)
(0.619)
0
3.26
1.10b
1.87
0.517
0. 764
0.624
1.04
)
6 m
0 :
0 :
0 i
0.665 ;
0
0
0
0.283
0.462
1.51 :
0.0348b
0.336
0.215
0.280
0.227
0.803
Values in parentheses are interpolations. Zeroes indicate no net mass
flux.
The filters for this run were lost; these estimates are based on scaling of
profiler wash catches by cyclone TP concentration.
53
-------
TABLE 3-12. PLUME SAMPLING DATA - PLANT AQ
Sampling rate (wVhr)
Run
AQl
AQ2
AQ3
AQ4
AQ5
AQ6
AQ7
AQ8
AQ9
AQ10
AQ11
U
G
S
X
U
G
S
X
p
G
S
X
G
S
X
c
p
G
S
c
p
G
S
C
P
G
S
X
p
G
S
X
G
S
X
c
G
S
X
c
G
S
X
c
1.5 m
20
20
20
20
23
20
20
27
17
19
48
35
27
30
37
27
34
25
27
20
20
19
20
29
35
28
28
20
20
19
17
28
28
33
30
20
20
23
19
32
32
32
32
3 m
39
39
39
39
28
28
27
35
29
29
-
40
34
33
43
42
38
36
43
24
24
19
19
43
53
42
42
24
26
25
25
40
40
46
43
40
43
35
40
26
56
54
54
4.5 n
51
51
51
51
35
32
34
34
38
-
37
37
48
43
34
51
53
40
44
53
22
22
22
22
50
51
51
51
26
28
29
29
47
47
56
50
52
42
57
"
36
36
37
36
6.m
58
58
53
58
40
-
39
39
46
-
43
43
51
47
42
54
59
42
51
59
24
24
34
24
59
70
56
58
27
31
-
36
.
49
60
54
61
-
-
61
40
-
-
40
Met TP exposure3 (nig/cm*)
1.5 in
1.80
2.29
0.992
4.34
4.02
3.06
0.982S
(3.10)b
2.78
0.341
0.256
0.189
10.5
2.39
6.72
6.43
4.91
1.66
3.07
7.91
3.43
3.55
2.32
7.70
1.61
1.18
2.24
5.03
1.63
0.586
1.03
3.10
1.36
0.317
0.152
0,379
1.48
0
0.453
0.539
2.69
0.980
0.690
0.641
3 m
1.60
0.848
0.593
2.45
2.38
2.81
0.491.
(2.60)°
1.56
0.324
0.218
(0.178)
6.23
2.03
4.43
4.24
3.56
1.30
2.08
6.28
2.34
2.90
1.79
5.85
1.35
2.13
1.89
3.41
0.867
1.23
1.10
1.87
0.529
0.143
0.0693
0.112
0.957
0.628
0
0.252
2.42
0.0274
0.216
0.346
4.5 m
0.815
0.0691
0.133
0.533
0.797
0.156
0.278
0.841
0.692
(0.0610)
0.104
0.104
1.93
0.980
1.60
0.634
2.30
0.395
0.933
2.47
0.904
1.73
0.644
2.25
0.547
0.854
0.755
1.12
0.306
0.378
0.564
0.523
0.219
0.0436
0
0.0550
0.350
0.122
0
(0.142)
0.561
0.0460
0
0
6 m
0.242
0
0
0.0888
0.391
0
0
0.092
0.194
0
0
0.0760
0.800
0.334
0.487
0.137
0.836
0.0872
0.388
0.295
0.383
0.635
0.300
0.418
0.153
0.320
0.179
0.0535
0.188
0.0828
0
0
0
0.0169
0
0
0.116
0
0
0
0.178
0
0
0
Values in parentheses are Interpolations; zeros indicate no net (i.e.,
downwind minus upwind) contribution.
These values based on linear interpolation/extrapolation using cyclone
and 4.5-m profiler values.
54
-------
TABLE 3-13. SURFACE PROPERTIES AND EMISSION FACTORS - PLANT AP
AP2 P
X
C
U .
APS P
X
C
U
APS P
X
AP6 P
X
ue
AP7 Pf.
XI
ue
Silt
content
(X)
1.9
d
2.7
8.1
2.6
d
4.3
8.3
6.1
11
6.8
10
7.3
11
12
6.0
Moisture
content
(X)
0.46
0.50
1.2
0.64
0.36
1.4
1.4
1.1
0.12
0.14
0.13
0.08
0.10
-
-
~
Total
1 oadi ng
(kg/o*)
6.4
0.73
5.4
6.7
8.5
0.88
4.6
6.3
4.8
2.0
8.7
5.1
2.0
_
-
2.09
Emission factor (a/VKT)3 <
Rawb
TP
179
-
.-
9,110
y- ' '
369
145
279
5,920
7-- •-'
2,330
6,460
1,480
3,720
711
877
346
1,430
*—
TP
182
-
-
10,600
468
184
355
7,390
2,170
6,010
1,290
3,240
620
871
843
1,470
.'Normal
IP
21.4
-
-
2,230
""''
55.3
30-5
-
1,550
5". : '-
412
1,200
118
620
18.0
81.8
101
220
1zedc
PMio
13.5 ,
-
-
1,810 :
- r - 'i
35.0
22.0
;
1,260 ;
- • '"•"
305
902
50.2
389
•B
65.1
82.6
162 '
FP
5.33
-
-
223
-'
13.8
3.07
-
155
"-
104
216
-
19.5
—
15,7
18.6
44.0
Blank entries denote cases of no net mass detected.
Because the normalization process does not affect size fractions, raw emission factors
for the other size ranges can be obtained by scaling the normalized values by the ratio
of the TP results.
c Normalized to 25 Mg (28 tons) and 12 wheels per vehicle.
d Less than 0.05%.
e These tests were characterized by a muddy and caked uncontrolled surface. See discus-
sion in text.
These sections had been flushed and vacuumed 3 days prior to test. See discussion in
Section 4.0.
" Estimated value.
55
-------
TABLE 3-14, SURFACE PROPERTIES AHO EMISSION FACTORS - PUNT AQ
Un.1 «4>t.w.M
^ ) i w rru , 9 ^ui V
content content
AQ1 U
S
S
X
AQ2 U
G
S
X
Aq3 P
G
S
X
AQ4 G
S
X
c
~AQS P
G
S
C
AQ6 P
G
S
_ c
AQ7 P
G
S
X
AQ8 P
G
S
X
AQ9 G
S
X
c
AQ10 G
S
X
C
AQ11 G
S
X
c
a Blank
E$ar»ait«
(X)
7,0
7.6
0.6
15
7.0
7.6
0.6
15
3.1
6.8
1.5
12
6.8
1.5
12
•*
5.0
10
4,4
12
5.0
10
4,4
12
3.6
7.0
2.9
6.7
3.6
7.0
2.9
6.7
0.76
1.2
1.1
1.6
2.9
-
-
-
2.9
->
-
"
entries
*A i>K*k «t#
T«*«1
Emission factor
-------
The five treated sections had a preapplieation average silt content of 13.9%
with a standard deviation of 2.2%. However, the uncontrolled tests at this
site were characterized by a silt content of approximately 7%. This lower
value may be the result of different drainage characteristics at the uncon-
trolled section. Because uncontrolled particulate emissions from unpaved
roads show a linear relationship with silt content,6 the mean uncontrolled
test results were scaled linearly to reflect a silt value of 13.9% in order
to relate control efficiency to the preapplication state.
Secondly, because of the muddy and caked conditions found on the uncon-
trolled surface at plant AP during August 1985, only the AP-2 and -3 uncon-
trolled tests were used in determining mean values.
57
-------
SECTION 4.0
CONTROL EFFICIENCIES AND COST
EFFECTIVENESS VALUES
This section uses the test results given in the preceding section to
develop control efficiency and cost-effectiveness values for the chemical
dust suppressants evaluated. Particular attention is paid to average con-
trol efficiency values which are required to estimate emission reductions
and cost-effectiveness. In addition, a discussion of control performance
indicators based on surface material properties is also presented.
It should be noted that this discussion is limited to the AQ series of
tests. As noted in preceding sections, the AP series was hindered by numer-
ous problems and, as a result, only a small number of tests was performed at
that site. Furthermore, because all five chemicals were evaluated concur-
rently at the AQ site, consideration of only tests at this site simplifies
comparisons. !
One important finding from the AP test series, however, should be men-
tioned. Run AP-7 was conducted on test surfaces which had been flushed and
vacuumed 3 days earlier. A control efficiency of 90% or more was found for
all size ranges considered. Thus, it would appear that paved road cleaning
techniques may be used to periodically increase the control efficiency of
chemically treated unpaved roads.
Control performance was examined in terms of several nonparametric sta-
tistical tests.10 A 10% level of significance (a = 0.10) was selected for
comparisons involving only one chemical; for comparisons ipvolving two or
more chemicals, a was set equal to 0.05 (5%).
Finally, calcium chloride is included only in the discussion of cost-
effectiveness. The rationale for this decision is based on the facts that
this product is not commonly used in the main iron and steel districts and
that, on the basis of the two test series, this product may not exhibit a
control lifetime similar to the other four suppressants evaluated.
The difficulties encountered due to the washoff of calcium chloride at
plant AP were described in Section 3.4. In addition, the heavy rains during
the second half of September may have seriously affected the control perfor-
mance of calcium chloride at plant AQ; emissions at the uncontrolled level
were found at the end of 1 month. However, it is important to note that the
test results from late October and early November show that calcium chloride
provided a level of control easily the equal of the other three suppressants.
During this time, the calcium chloride test section appeared visibly wet
even after 3 to 4 hr of steady traffic. '
58
-------
4.1 COMPARISONS INVOLVING ONLY ONE CHEMICAL
This section presents a discussion of comparisons of point (instantane-
ous) values of control efficiency for a single dust suppressant. The most
important comparison involves temporal dependence of control efficiency be-
cause the results obtained determine the functional forms for c(t) and C(T),
as defined in Section 2.7. In cases of significant decay with time, in-
stantaneous and average control efficiency are treated as linear functions
of time. Otherwise, average efficiency is considered as a constant value
over the time period of interest. I
Other comparisons involving a single dust suppressant are also de-
scribed. These examine whether there is a significant difference in a prod-
uct's control performance for various size fractions and whether a repeat,
higher intensity application results in a higher level of control.
To assess the time dependence of control, the results of runs AQ-1 to
-4 were compared to those of AQ-5 to -8. These two groups of tests were
conducted at 13 to 14 and 29 to 30 days after application, respectively.
The Mann-Whitney U test11 was employed to determine whether control perfor-
mance for a given chemical decreases with time over the first 30 days after
application. The results of the comparisons for TP and PM10 are shown below:
Significant
decrease in
Chemical Size fraction control :
P TP no :
PM10 no ;
G TP no ;
PM10 no
S TP yes
PM10 yes
X TP no ;
PMio yes
3 One-tailed alternative hypothesis. \
Only three comparisons showed a decrease (significant at the 10% level) in
control over the time period of interest. As noted earlier, this informa-
tion was used to determine the forms for the average control efficiency
functions presented in the next section, ;
59
-------
A U test of the results from runs AQ-1 to -4 and AQ-9 to -11 wag also
performed. Because all these ttsts were conducted at approximately the same
time after application, this comparison tested whether there was a signifi-
cant increase in control after the repeat application in October. All com-
parisons showed increases significant at the 5% level.
To determine whether control efficiency is significantly different
for various size ranges, a two-way analysis of variance by ranks (Friedman
test11) was employed with a two-tailed alternative hypothesis. Of the four
chemicals, only Soil Sement exhibited a significant (a = 10%) difference in
control between the TP, IP and PM10 siii fractions during the period of runs
AQ-1 through -8. No significant differences were found for runs AQ-9 through
-11. I
4,2 AVERAGE CONTROL EFFICIENCY :
Least-squares lines of best fit were developed for those suppressant/
size fraction combinations which showed significant temporal dependence
over the period of AQ-1 to -8. Average control efficiency functions were
then determined from the lines of best fit. In instances where no signifi-
cant difference with time was found, all control efficiency values were
averaged and the mean value applied over the period of the first eight runs
(i.e., first 13 to 30 days after application).
Figure 4-1 presents average, size-specific control efficiency as a
function of time for the four dust suppressants. Because of the nature of
the field program conducted at plant AQ, these functions may be viewed as
representative of typical values of application and dilution rates, daily
traffic volume and average vehicle weight for unpaved roads in the iron and
steel industry. Furthermore, because most plants in the industry reapply
chemicals within 1 month (cf Table 2-1), these average control efficiency
functions should be of considerable use to both iron and steel and regula-
tory agency personnel in assessing the emission reductions and cost-
effectiveness of dust control programs.
As can be seen from the figure, average control values for the four
dust suppressants tend to be more closely clustered for the IP and PM1(> size
fractions than for the two extreme size ranges (TP and FP). The question of
whether there is a significant difference in control performance between the
various suppressants is addressed in the following section. '•
As a final remark, it should be noted that these results are based upon
field tests after a second control application. Thus, some residual effect
of the initial application is included. A further discussion of the effect
of repeated applications on average control efficiency is presented in Sec-
tion 5.0.
4.3 INTERCHEMICAL COMPARISONS
The results given as Figure 4-1 indicate the dust suppressants evalu-
ated during this program exhibited varying levels of control over the time
60
-------
/oe
See Sec&en 3.4
Figure 4-1. Average control efficiency as a function of time over 30 days.
61
-------
period covered by runs AQ-1 to -8, This section discusses whether there are
significant differences between the products in terms of control perfor-
mance. In order to keep the number of comparisons at a manageable level,
only TP and PM10 control efficiencies are considered. :
In making interchemical comparisons it was first necessary to factor
out any temporal variation. This step was required simply because (a) it
was not possible to evaluate each chemical during each run (i.e., fewer
profilers available than test sections during the first eight tests) and
(b) some chemicals exhibited time-dependent control. For runs AQ-1 to -8,
normalized emission factors were scaled by the mean (controlled) emission
factor observed during the test. A two-tailed U test was then applied to
the six pairwise combinations of suppressants. Note that a two-tailed test
was employed because there was no a priori reason to suspect that one chemi-
cal suppressant would perform "better" than another. The results of these
comparisons for runs AQ-1 to -8 are shown below: :
Chemicals Size Significant ;
compared fraction di fference •
P and G TP no
PM10 no ;
P and S TP noj:
PM10 noc ;
P and X TP no ;
PM10 no ;
G and S TP noc j
PM10 yes
G and X TP noc j
PM10 no :
S and X TP yes
PM10 yes ;
a See caution at the end of this section.
Two-tailed alternative hypothesis. :
c Significance levels between 5 and 10%. ;
Although only three of the comparisons showed significant (at the 5%
level) differences in control performance, it should be noted that all com-
parisons involving Soil Sement show differences at the 10% level of sig-
nificance. Only one pairwise comparison involving the other three sup-
pressants exhibited a difference significant at this level. |
62
-------
A final interchemical comparison involved the results of runs ^C"'
through -11. After the October rain damage, only four test sections -*'
mained. Because all suppressants were evaluated simultaneously dun>c "*
final three runs, there was no need to consider temporal variation be:***"
tests. The Friedman test indicated no differences in either TP or PH.- --*1"
trol at the 5% level of significance.
The reader is cautioned against the possible misuse of these res-"** to
conclude that one dust suppressant is "better" than another The re'lar**
cost-effectiveness of each chemical is an important consideration whe« se-
lecting a product for use in a dust control program. For example a 1 .>«""
priced chemical with moderate performance characteristics may be 'prefe?"**c
to- a highly effective yet more expensive product in a situation where
-------
The following table gives 1985 costs for the dust suppressants evalu-
ated during the program:
Cost ($/gal)
Suppressant
P
G
S
X
C
Small lot3
3.40
1.65
2.32
2.10,
0.70d
8ulkb
1.50,.
c
1,49
1,45,
0.46d
FOB costs for 55-gal. drums (except C). ;
Data taken from MRI's cost records for the
field program.
Data developed from telephone conversations
with vendors and plant personnel. All
prices FOB, except as noted.
c No plant currently produces this product. ,'
See discussion in text.
Cost includes delivery and application.
Because no one currently produces generic products, definitive costs
are unavailable. The Mellon Institute has estimated that on-site production
of this type of product would cost between $1.14 and $0.86/gal., with re-
quired capital costs ranging from $9,000 to $30,000, respectively.5 The
actual cost per gallon would be a function of the production rate and would
decrease after the capital costs are recovered. In addition, it may be
feasible for neighboring plants to pool their resources and retain an out-
side firm to produce the product. Because of the variety of costs possible,
a value of $1.15/ga1. has been assigned. This value represents a 5<-year
average for an annual on-site production of 20,000 gal.
The reader should, of course, assess all costs involved in any specific
program in which the use of a generic product is contemplated. The price
given above has been assigned only for the purpose of comparison.
As a final remark about dust suppressant costs, note that the cost for
calcium chloride includes delivery and application. This fact, combined
with a substantially lower unit cost, has resulted in some interest in the
industry in the use of salts for unpaved road dust control.
64
-------
For runs AQ-1 through -8, the following cost-effectiveness values were
obtained:
30-Day
Unit application cost-effectiveness rs/kol
Suppressant costs ($/km) TP IP PM
p
G
S
X
C
1,720_
1,640C
2,150
1,720
2,190
0.17 0,65 1.00 :
0.15 0,64 1.03
0.15 0.63 0.91 ;
0.21d 0.57. 0-87.
0.24a i.iod 1.61d
3 Based on application of September 3, 1985. Bulk costs used with 10% !
increase for delivery (except C) and $0.15 per gallon of concentrate
for application. See discussion in text.
Cost per kg of emissions reduced. Based on 30-day average control,
ISO vehicles per day and the mean, normalized uncontrolled emission ;
factors in Section 3.0.
c Assumes no delivery cost (on-site production). See text for discussion
of chemical cost. !
Because this application produced a control lifetime of i»ss than 30 days,
an average control of 50% has been assigned. The resulting cost-effective
ness values should be considered lower bounds.
Several remarks about this table are in order. First, the increases ;
over bulk costs for delivery and application are based on delivered prices
quoted by consumers and vendors as well as past MRI data.1'2 Qnce again,
the geographic location and any particular application requirements for a
plant may change these values. The 30-day cost-effectivenes-, values may be
scaled using other delivery and application cost data.
Second, although the road section treated with calcium chloride had the
highest unit cost, this does not necessarily imply that this treatment would
always be the most expensive. The other chemicals may requi^a storage fa-
cilities and application equipment which could substantial]/ "increase the
total cost per treatment.
For the first test series (AQ-1 to -8), the cost-effect /eness values
for calcium chloride do not compare favorably with those of *ne other sup-
presants. As noted above, the cost-effectiveness values for calcium chlo- ;
ride may be considered lower bounds and are between 40 and 20% higher than
the average values for the other four suppressants. However, as discussed
in Section 3.4, rainfall during this period was 160% of norms': with a total
of 108 mm (4.27 in.) of rain falling in the 2 weeks between -tins AQ-4
65
-------
AQ-5. It is possible that this product would have produced more favorable
cost-effectiveness values under drier conditions as was the case during runs
AQ-9 through -11.
Cost-effectiveness values associated with runs AQ-9 through -11 are not
particularly relevant because weather curtailed field testing long before
an adequate description of decay (and, thus, control lifetime) could be
obtained. However, it is possible to use results of earlier testing of
Coherex® at the AQ test site to estimate relative cost-effectiveness for
different application intensities.1 Although these earlier tests involved
a substantially higher average vehicle weight, the AQ series of tests was
characterized by a higher daily traffic rate. Assuming that these differ-
ences balance one another suchthat the control decayand the daily emission
rates are comparable, the following results are obtained:
30-Day average ;
Unit application control (X) •.
Test series cost ($/kni)a TP IP PMin
AQ1-8 l»720b 47 75 73
Reference 1 4,750C 59 72 76
Reference 1 3,580d 93 92 94
Developed using 1985 cost data and procedure described earlier.
Repeat application of September 3, 1985.
c Initial application of 3.8 L/m2 (20% solution).
Repeat application of 4.5 L/m2 (12% solution).
Under the assumptions of comparable decay and emission rates, it would
appear that lighter application intensities are more cost-effective over a
30-day period, which is a fairly typical time interval between treatments in
the iron and steel industry (see Table 2-1). This is particularly true for
the smaller particle size ranges.
4.5 EXAMINATION OF ALTERNATIVE INDICATORS OF CONTROL PERFORMANCE
Because of the high costs associated with conducting field tests of
emissions from controlled unpaved roads, there has been a great deal of
recent interest in developing less expensive measures of control perfor-
mance. For example, suppose that a reliable estimate of a suppressant1s
effectiveness (at a given point in time) could be obtained by simply ex-
amining the quantity and/or texture of aggregrate material present on the
road surface. This technique would be an invaluable tool to both industry
and regulatory personnel in monitoring dust control programs. ':
66
-------
Early studies of unpaved road dust control showed a strong correlationj
between efficiency and the silt content of the surface material.2'12 How-
ever, it must be noted that these relationships were based on the very high.
(e.g., > 90%) control efficiencies and very low silt values typically founii
over the first few days after application. Because these conditions repre
sent only a small, restricted portion of all possible conditions, the high
degree of correlation is somewhat misleading. \
Later study of long-term control indicated no significant correlation
between silt content and efficiency. In addition, fairly high (~ 50%) con
trol efficiencies were found to occur with silt contents at or above the un
controlled level.1 Because of these findings, attention turned to the use
of the amount of silt per unit area as a performance indicator.
Figure 4-2 presents the relationship between controlled PM10 emission
factors (and, hence, control efficiency) and silt loading for Runs AQ-1
through -8. The arrows connecting like data points denote the time history
As can be seen, although emission levels vary over an order of magnitude,
silt loading values vary over two orders and do not appear to follow a spe
cific trend with time. Furthermore, the results for the different suppres
sants tend to be clustered together; this would indicate that the various
suppressant types do not affect silt loading in the same way. The procedure
presented in Section 4.3 was used to compare silt loadings on the differeru
surfaces. Silt loadings on the surface treated with Soil Sement differed
from those on both the Coherex® and Generic sections at a significance
level of 5%. In the comparisons involving the Petro Tac surface, no sig-
nificant differences were found.
Reasonably strong correlations between silt loading and emissions ap~
pear to exist for both Soil Sement and Coherexi; however, the two relation :
ships bear little resemblance to one another. For example, both products
appear to produce essentially the same level of control although the silt
loading values differ by a factor of 50. ;
It does not currently appear possible to develop a meaningful expres-
sion that relates the control performance of chemical suppressants to the
amount of silt loading present on the road surface. When suppressants art-
considered individually, only Coherex® exhibits a significant correlation.
This is not the case, however, when the other petroleum resin (Generic) i-,
considered as well.
Finally, it should be noted that the AP-42 industrial paved road emis
sion factor equation may be used to conservatively overestimate control lee
emissions. This equation is shown in Figure 4-2, over the range of silt
loading values in the supporting data base. As can be seen, the equation
tends to overestimate emissions by a factor of 1.5 to 2 when applied to
situations with typical application intensities over the first 30 days.
Estimation is appreciably better for the Soil Sement section, possibly be-
cause the silt loadings associated with this surface more closely match
typical values for paved roads.6 Silt loadings for the other three test
sections are clustered near the extreme value used in developing the pave-;
road equation. As a result, it may not be particularly surprising that th*;
paved road equation overestimates emissions from these surfaces to a great*-
degree. •
67
-------
en
oo
10
w
v>
•««,
a
O.I
/o
to
~3
to
0.1
/o
Figure 4-2. Relationship between controlled PM10 emission factors and silt loading.
Arrows indicate chronology of testing.
-------
SECTIi,,, .
>. j
CONTROL PERFORMANCE
5E. 3EVELOPMENT
estimating the average dust control
many field tests of road dust control -
other industries, it is unportant to .
only provide one estimate of average <.,
ply because the van ous test results
must be combined to obtain a decay rat.
base of, say, 100 controlled tests, v
10 pieces of information about average .
program was to examine means of
of a suppressant. Although
conducted in the steel and
that a given test series can
efficiency. This is true sim-
times after application
although there may be a data
iata base may only provide,
trol.
Jhe following sections discuss t,, . t1tfes of thc model examine
previous studies of unpaved road dust .Vt°J effectiveness for inclusion in
the model, and, finally, present the m^, u
S.I OBJECTIVES OF THE AVERAGE CONTROL
>CORMANCE MODEL
It is generally conceded that th«= . .. f t influence aver-
age control performance for a chemica^ •_°*up*ressant ove^ t1me;
01rect relations hip
Amount of chemical applied per
unit area
Number of previous applications
Indirect relationship
•rage vehicle weight
>rage number of wheels per vehicle
• ly traffic volume
There are, of course, other van's,
treated surface aggregate, applicatior
may influence average control. Howeve
these variables affect control and the-
draw upon. Consequently, no attempt h&
in the model development.
(such as the texture of the un-
' tadures or dilution ratio), that
is not intuitively obvious how
only a very limited data base to
•*«n made to include these factors
,. UJS ^antageous to combine som, , /3riables shown in the ear-
lier table. For example, MRI's expen*,: - ft a hi n intercorrelation
between average vehicle weight and avfc « Qf wheels> Th as a
first approximation, only one of the t> --
69
-------
In addition, because of the limited data base available, it is benefi-
cial to combine traffic volume and average vehicle weight into a singpe mea-
sure of the service environment of the test road. Let the quantity
/Average Weight ] w /Vehicles
I per Vehicle I I per Day
be defined as V, the vehicle activity factor. The data presented in Sec-
tion 2.1 show a mean V for the 9 iron and steel plants of 2,400 Mg/day
(2,600 ton/day) with a 91% coefficient of variation. ;
Finally, because reapplications of dust suppressants have been found to
be more effective than the original application, a new factor was devised
for the two variables shown as having direct relationships with average con-
trol. This factor, g, is termed the (cumulative) ground inventory and is
found by adding together the total volume (per unit area) of concentrate
(not solution) since the start of the dust control season. For example, if
a plant originally applied 2 L/m2 of a 20% solution on April 1, and followed
with 1.5 L/m2 of a 16% solution on the first of each following month, then
after the June 1 application, g = 0.88 L/m2.
5.2 REVIEW OF PREVIOUS STUDIES
The first field evaluation of unpaved road dust control in the iron and
steel industry was conducted in August 1978. Since that time, dozens of
additional tests have been conducted in the industry; Table 5-1 summarizes
those tests. As can be seen, several studies entailed evaluation only a
short time after application; some tested only light-duty traffic; and still
others did not provide enough information on either application parameters
or the service environment of the test road.
The primary selection criteria for inclusion of data in the model de-
velopment pertained to (a) spanning a period somewhat representative of the
time intervals in the iron and steel industry between applications, and
(b) reporting the information needed for model development.
Upon review of controlled emission tests in the iron and steel indus-
try, it soon became obvious that only petroleum resins have been evaluated
in enough studies to warrant an attempt at model development. Furthermore,
this fijiding was unchanged when tests in other industries were consid-
ered.15 1S As a result, only results from tests of petroleum resins in the
iron and steel industry are considered below. Note that tests of generic
formulations are included with Coherex® because no significant difference
was found between the two in Section 4.3.
Table 5-2 presents average efficiency values for TP and PM10 control
from tests of petroleum resins in the iron and steel industry. Correlations
are given below:
70
-------
TABLE 5-1. SUMMARY Of MAJOR UNPAVEO ROAD CONTROL EFFICIENCY TESTS PERFORMED AT IRON AND STEEL PLANTS
Research
organization
HRI»'2'a
Mellon
Institute* "a
EIAn..s
The main test
b .
Oust
suppressant
tested
Cohere x8
Co he rex®
Coherex®
Cohere*©
Petro T«
Cohere**
Oil well brine
flrcote 210/
Flambinder
Generic 1
Cone rex®
Generic 1
Co he rax®
CoherexD
CoheraxA
section of the road
No, of
valid
control 1 ed
tests
2
9
6
4
8
5
5
5
3
3
1
1
4
z
Test site
Araco-Mi ddl e ton
Armco-M i dd ) e ton
Armco-Kansas City
Arnco- Kansas City
J&L Indiana Harbor
Shenango
Shenango
Shenango
Shenango
Shenango
Shenango
Shenango
Shenango
US S- Homestead
was retreated 44 days after the
Measurement
method
Profiling
Profiling
Profiling
Profiling
Profiling
Upw jnd/downwind
Upw i nd/downw i nd
Upw i nd/downw 1 nd
Profiling
Profiling
Profiling
Profiling
Prof 11 ing
Profiling
Tine after
application
(days)
< 7
1-1
7-41
4-35
Z-116
3-30
3-30
3-30
3-21
3-21
9
9
Unknown
14-15
Application
intensity
(gal. iol./yd1)
Unknown
0,19
0.83 (initial)
1.0 (repaat)
0.70
1.5
3.8
1.9
O.S1 (Initial)
0.52 (initial)
0.36 (repeatr
0.36 ( repeat )c
Unknown
Unknown
Dilution ratio
(gal. chant: gal, H20)
1:9
1:6
1:4 (Initial)
1:8 (repeat)
1:4
1:4
Naat
1:4
1:9
1:9
1:12
1:12
Unknown
1:4 - 1:7
Avg. vehicle
weight (ST)
3
3-54
27-50
31-56
23-34
3
3
3
3b
3
3b
4-19
26
initial application at the Kansas City Works.
c Road retreated 24 days after initial application.
-------
TABLE 5-2. AVERAGE CONTROL EFFICIENCY FROM TESTS OF PETROLEUM RESINS3
IN THE IRON AND STEEL INDUSTRY
Average control (%) over i
nominal period ,
V
(Mg/day)
850
2,000
2,400
3,200
3,900,
500T
2,000
2,400.
500T
g
(L/m*)
1.3
0.43
1.6
0.75
1.2
0.24.
0.4Sd
i.oa
0.23e
14-Day
TP
96b
47
98
70
95
94
59
88
89
PM10
86
99
86
96
-
60
84
<*"
30- Day
TP
90b
47
-
59
93r
87C
59
77C
PMio
73
-
76
94
_
60
-
**"
Reference
13
AQ-1 to -8
AQ-9 to -11
1
1
5
AQ-1 to -8
AQ-9 to -11
5
a All Coherex®, except as noted.
b TSP values.
c Extrapolated from 21 days after application. ;
Generic 2 (QS) formulation. !
e Original generic formulation. :
Assumes value of 2.7 Mg/vehicle. Average weight not given in reference.
72
-------
Size
fraction
TP
PM10
Nominal
averaging
period
14 days
30 days
14 days
30 days
Correlation of
control with
V
-0.14
0.51
-0.17
0.88
2
0.50
0.76
0.45
0.91
Sample
size
9
7
6
4
The expected (i.e., negative) correlation between control and V was
found only in two cases and, in each of those cases, the degree of linear
correlation was very weak. Because of this finding, V was dropped from con-
sideration as a model parameter.
Additional remarks concerning Table 5-2 are in order. The vehicle
activity factors for Reference 5 are based on an assumed average weight
of 2.7 Mg (3 tons) because no other information was provided. Also, the
results from these two tests appear inconsistent with the other studies; in
fact, if the two results are deleted from the data base, average control and
V show a very weak, positive correlation. For these reasons, the results:
from Reference 5 were excluded in developing the model. i
5.3 AVERAGE CONTROL MODEL FOR PETROLEUM RESINS
The data base discussed in the preceding section was used in developing
the least-squares models of average control performance presented in Fig-
ure 5*1 and shown below:
Nominal
Size averaging Sample Estimated average Correlation
fracti on ^period si ze efficiency (%) coefficient
TP 14 day 7 37 + 44 g 0.948
30 day 5 28 + 52 g 0.939
PM10 14 day 6 64 + 23 g 0.755
30 day 4 50 + 36 g 0.915
a The variable "g" represents ground inventory (L/m2). See
text for a discussion of g.
These TP models all show correlations significant at the 2% level, while
for PM10, the corresponding level is only 10%.
73
-------
IQQ
0
Ui
2
Qe
*
I
ICO
\
Q
0.5
\
I
/S
Figure 5-1, Average control performance model for petroleum resins.
74
-------
It is important to note that these models are largely designed to
typical needs in the iron and steel industry. The two averaging period* att*
representative of common time intervals between control treatments in *^e
industry. Also, the vehicle activity factor values supporting the racers
have a mean of 2,650 Mg/day, which closely matches the mean /alue for *-1e
traffic study data presented in Section 2.1.
How well the model performs for vastly different service environments
(e.g., western surface mining or unpaved rural roads) is not known at this
time. As a result, the reader should be cautious when applying this model
to situations far different than the conditions of the underlying data b
-------
SECTION 6.0
REFERENCES
1. Muleski, G. E., T. Cuscino, Jr., and C. Cowherd, Jr. Extended Evalua-
tion of Onpaved Road Dust Suppressants In the Iron and Steel Industry,
EPA-600/2-84-027 (NTIS PB84-154350), U. S. Environmental Protection
Agency, Research Triangle Park, North Carolina, February 1984.
2, Cuscino, T., Jr., G. I. Muleski, and C. Cowherd, Jr. Iron and Steal
Plant Open Source Fugitive Emission Control Evaluation. EPA-6QQ/2-83-
110 (NTIS PB84-110568), U. S. Environmental Protection Agency, Research
Triangle Park, North Carolina, October 1983.
3. Cowherd, C., Jr., R. Bonn, and T. Cuscino, Jr. Iron and Steel Plant
Open Source Fugitive Emission Evaluation. EPA-600/2-79-103 (NTIS
PB299385), U. S. Environmental Protection Agency, Research Triangle
Park, North Carolina, May 1979.
4. Bonn, R., T. Cuscino, Jr., and C. Cowherd, Jr. Fugitive Emissions from
Integrated Iron and Steel Plants. EPA-600/2-78-Q50 (NTIS PB281322),
U. S. Environmental Protection Agency, Research Triangle Park, North
Carolina, March 1978.
5. Russell, D., and S» C. Caruso. The Relative Effectiveness of a Dust
Suppressant for Use on Onpaved Roads Within ths Iron and Steel Indus-
try. Presented at SPA/AISI Symposium on Iron and Steel Pollution
Abatement. Cleveland, Ohio, October 1984.
6. Environmental Protection Agency. Compilation of Air Pollution Emission
Factors, Volume I, AP-42 (GPO 055-000-00251-7), Research Triangle Park,
North Carolina, September 1985.
7. Kolnsberg, H. J. Technical Manual for Measurement of Fugitive Emis-
sions: Upwind/Downwind Sampling Method for Industrial Emissions, ;
EPA-600/2-76-089a (NTIS PB253092), U. S. Environmental Protection
Agency, Research Triangle Park, NC, April 1976.
8. Cowherd, C., Jr., K, Axetell, Jr., C. M. Guenther (Maxwell), and G.
Jutze. Development of Emission Factors for Fugitive Dust Sources.
EPA-450/3-74-037 (NTIS PB238262), U. S. Environmental Protection
Agency, Research Triangle Park, North Carolina, June 1974, ;
9. Dmvies, C. N. The Entry of Aerosols in Sampling Heads and Tubes-
British Journal of Applied Physics, 2;921 (1968). !
76
-------
10. Muleski, G. E, Critical Review of Open Source Particulate Emissions
Measurements: Field Comparison. MRI Final Report Prepared for Southern
Research Institute, MRI Project No. 7993-L(2). August 1984.
11. Hollander, W., and J. Wolfe. Nonparaaetric Statistical Methods. J
Wiley and Sons, New York, 1973. ;
12. Cuscino, T., Jr., G. 1. Muleski, and C. Cowherd, Jr. Determination of
the Decay in Control Efficiency of Chemical Dust Suppressants on On-
paved Roads. Ins Proceedings: Symposium on Iron and Steel Pol-
lution Abatement Technology for 1982. SPA-600/9-33-016 (NTIS PB83-
258665), 0. S. Environmental Protection Agency, Research Triangle Park,
North Carolina, April 1983, pp. 136-148.
13. Russell D., and S. C. Caruso. A Study of Cost-Effective Chemical Dust
Suppressants for Use on Unpaved Roads in the Iron and Steel Industry.
Report prepared for the American Iron and Steel Institute, December
1982.
14. Energy Impact Associates. An Alternative Emission Reduction Option for
Shenango Incorporated Coke and Iron Works, January 1981.
15. Eoffman, A., et al. A Study of Controlling Fugitive Dust Emissions
from Nontraditional Sources at the United States Steel Corporation Fa-
cilities in Allegheny County, Pennsylvania. Report prepared for U. S.
Steel Corporation, 600 Grant Street, Pittsburgh, Pennsylvania, December
1981.
16. Schanche, G. W., M. J. Savoie, J. E. Davis, V. Scarpetta, and P. Weggel.
Unpaved Road Dust Control Study (Ft. Carson, Colorado). Draft Final
Report, U. S. Army Construction Engineering Research Laboratory,
Champaign, Illinois, October 1981.
17. Rosbury, K. D., and R. A. Zinuaer. Cost-Effectiveness of Dust Controls
Used on Onpaved Haul Roads - Volume 1 of 2. Draft Final Report for
U. S. Bureau of Mines, Minneapolis, Minnesota, December 1983.
18. Axetell, K. H., and C. Cowherd, Jr. Improved Emission Factors for
Fugitive Dust from Western Surface Coal Mining Sources, EPA-600/7-
84-048 (NTIS PB84-170802), D. S. Environmental Protection Agency,
Cincinnati, Ohio, July 1984.
19. Cuscino, T-, Jr. Taconite Mining Fugitive Emissions Study. Minnesota
Pollution Control Agency, Roseville, Minnesota, June 1979.
77
-------
SECTION 7.0
GLOSSARY
Application Frequency - Number of applications of a control measure to a
specific source per unit time; equivalently, the inverse of time be-
tween two applications.
Application Intensity - Volume of water or chemical solution applied per
unit area of the treated surface.
Control Efficiency, Average - Mean value of the (instantaneous) control ef-
ficiency function over a specified period of time.
Control Efficiency, (Instantaneous) - Percent decrease in controlled emis-
sions at a given instant in time from the uncontrolled state,
Cost-Effectiveness - The cost of control per unit mass of reduced particu-
late emissions.
Decay Rate - The absolute value of the slope of the (instantaneous) control
efficiency function.
Dilution Ratio - Ratio of the number of parts of chemical to the number of
parts of solution, expressed in percent (e.g., one part of chemical to
four parts of water corresponds to a 20% solution).
Dry Day - Day without measurable (0,01 in. or more) precipitation.
Dry Sieving - The sieving of oven-dried aggregate by passing it through a
series of screens of descending opening size.
Oust Suppressant - Water or chemical solution which, when applied to an ag-
gregate material, binds suspendable particulate into larger less sus-
pendable particles.
Exposure - The point value of the flux (mass/area-time) of airborne particu-
late passing through the atmosphere, integrated over the time of mea-
surement.
Exposure, Integrated - The result of mathematical integration of spatially
distributed measurements of airborne particulate exposure downwind of
a fugitive emissions source.
78
-------
Exposure Profiling - Direct measurement of the total passage of airborne
participate immediately downwind of the source by means of simultaneous
multipoint isokinetic sampling over the effective cross-section of the
emissions plume.
Exposure Sampler - Directional particulate sampler with a fiberglass intake
serving as a settling chamber followed by a backup filter. The sampler
has variable flow control to provide for isokinetic sampling at wind
speeds of 1.8 to 8.9 m/s (4 to 20 mph).
Fugitive Emissions - Emissions not originating from a stack, duct, or flue.
Moisture Content - The mass portion of an aggregate sample consisting of un-
bound surface moisture as determined from weight loss in oven drying.
Normalization - Procedure that ensures that emission reductions not attrib-
utable to a control measure are excluded in determining an efficiency
of control.
Particle Diameter, Aerodynamic - The diameter of a hypothetical sphere of
ynit density (1 g/cm3) having the same terminal settling velocity as
the particle in question, regardless of its geometric size, shape, and
true density. Units used in the report are microns aerodynamic (umA).
Particulate, Fine - Airborne particulate smaller than 2,5 urn in aerodynamic
diameter. ;
Particulate, Inhalable - Airborne particulate smaller than 15 ym irt aerody-
namic diameter. :
Particulate, PM10 - Airborne particulate smaller than 10 urn in aerodynamic
diameter. :
Particulate, Total - All airborne particulate regardless of particle size.
Particulate, Total Suspended - Airborne particulate matter as measured by a
standard high-volume (hi-vol) sampler. •
Road, Paved - A roadway constructed of rigid surface materials, such as
asphalt, cement, concrete, and brick.
Road, Unpaved - A roadway constructed of nonrigid surface materials such as
dirt, gravel (crushed stone or slag), and oil and chip surfaces.
Road Surface Dust Loading, Paved - The mass of loose surface dust on a paved
roadway, per length of roadway, as determined by dry vacuuming preceded
by broom sweeping, if necessary.
Road Surface Oust Loading, Unpaved - The mass of loose surface dust on an
unpaved roadway, per unit area, as determined by broom sweeping.
79
-------
Road Surface Material - Loose material present on the surface of an unpaved
road.
Silt Content - The mass portion of an aggregate sample smaller than 75 mi-
crometers in diameter as determined by dry sieving.
Silt Loading - The mass of loose surface dust per unit area on a road multi-
plied by its silt content, ;
Source, Open Dust - Any source from which emissions are generated by the;
farces of wind and machinery acting on exposed aggregate materials.
Vehicle, Heavy-Duty - A motor vehicle with a gross vehicle travelling weight
exceeding 30 tons.
Vehicle, Light-Duty - A motor vehicle with a gross vehicle travelling weight
of less than or equal to 3 tons.
Vehicle, Medium-Duty - A motor vehicle with a gross vehicle travelling
weight of greater than 3 tons, but less than 30 tons.
SO
-------
SECTION 8.0
ENGLISH TO METRIC UNIT CONVERSION TABLE
English unit Multiplied by
gal /yd2
Ib/vehicle mile
Ib
ton
mph
mile
ft
gal.
yd2
4.53
0.282
0.454
0.907
0.447
1.61
0.305
3.78
0.836
Metric unit
L/m2
kg/vehicle km
kg
Mg
tn/s
km
m
L
m2
Example: 5 miles x 1.61 = 8 km.
81
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/2-87-102
3. RECIPIENT'S ACCESSION1 NO.
4, TITLS AND SU1TITLE
Evaluation of the Effectiveness of Chemical Dust
Suppressants on Unpaved Roads
5. REPORT DATE
November 1987
6. PERFORMING ORGANIZATION CODE
7. AUTHORtS)
G. E. Muleski and C. Cowherd, Jr.
8. PERFORMING ORGANIZATION REPORT NO.
8127- L (MRI)
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Midwest Research Institute
425 Volker Boulevard
Kansas City, Missouri 64110
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
P.O. 5200-868236*
12. SPONSORING AGENCY NAME AND ADDRESS
EPA, Office of Research and Development
Air and Energy Engineering Research Laboratory
Research Triangle Park, NC 27711
13, TYPE OF REPORT AND PERIOD COVERED
Project Report; 6/84 - 11/86
14. SPONSORING AGENCY CODE
EPA/600/13
is. SUPPLEMENTARY NOTES £EERL project officer is Robert C. McCrillis, Mail Drop 62B.
919/541-2733,, (*) LTV Steel Co. (formerly Jones and Laughlin Steel Corp.) penalty
«. ABSTRA
report gives results of measurements of the long-term effectiveness
of five unpaved-road chemical dust suppressants. Effectiveness at controlling total
particulat'e emissions in three size fractions (<15, <10, and <2.5 micrometers)
was determined over several cycles of chemical application, control effectiveness
decay, and chemical reapplication. All five chemicals were tested on the same road
with each chemical used on separate abutting road segments. The chemicals were
applied in quantities that spanned the range of common practice in the steel indus-
try. Traffic parameters were typical of the steel industry. Over a 30- day period,
control effectiveness of each chemical decreased; in some cases by as much as 50%,
and in others by as little as 10%. Control effectiveness for all chemicals was > 95%
immediately after chemical application or reapplication. The rate of decay was
about the same for all particle size ranges investigated. Road surface silt loading
was found to be a reliable indicator of relative effectiveness for some chemicals,
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b. IDENTIFIERS/OPEN ENDED TERMS
c. COSATl Field/Group
Pollution
Dust
Roads
Chemical Compounds
Iron and Steel Industry
Pollution Control
Stationary Sources
Dust Suppressants
Unpaved Roads
13B
11G ;
07B,07C
11F
18. OISTRI1UTION STATEMENT
Release to Public
19. SECURITY CLASS {This Report)
Unclassified
21. NO. OF PAGES
91
20. SECURITY CLASS (TMs page}
Unclassified
32, PRICE
EPA Form 2220-1 (9-73)
82
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